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School: Stanford
Course: The Fourier Transform And Its Applications
EE261 Raj Bhatnagar Summer 2009-2010 EE 261 The Fourier Transform and its Applications Midterm Examination 19 July 2010 (a) This exam consists of 4 questions with 12 total subparts for a total of 50 points. (b) The questions dier in length and diculty. Do
School: Stanford
x h s w e p x h s s u w j n h x h n h x j x o x x o h h m m o h x x x r x x x j i x o j n x k x f f u i i w g i u o n x x x x i n x h o x x j o f j i w i j x x k m j k k x n m j j h x p h x f n r x n n i m n n q q w f j h k o f j w l n n k f j o j m x i
School: Stanford
Course: INVESTMENT SCIENCE
Lecture #9 Markowitz Portfolio Theory Reading: Luenberger Chapter 6, Sections 6 - 10 Primbs/Investment Science 1 The Markowitz Model Solving the Optimization The Two Fund Theorem Markowitz Portfolio Theory Inclusion of a Risk Free Asset The One Fu
School: Stanford
Course: Data Analysis
STATS 202 Homework 1 Hao Chen July 3, 2011 In total: 40 points. Problem 2 (26 points, 2 points each) Classify the following attributes as binary, discrete, or continuous. Also classify them as qualitative (nominal or ordinal) or quantitative (interval or
School: Stanford
Course: Modern Applied Statistics: Learning
STATS 315A Winter 2007 Homework 1 Solutions Prob. #1 (Thanks to Wei Zhen) (a) The function mixG takes a centroid matrix mu, a vector N specifying the number of samples in each group and the noise variance v. mixG <- function (mu, N, v)cfw_ mu <- rbind(mu)
School: Stanford
Course: LINEAR AND NON-LINEAR OPTIMIZATION
MS&E 211 Linear & Nonlinear Optimization Fall 2011 Prof Yinyu Ye Homework Assignment 3: Due Tuesday November 1st at 6:00pm Problem 1. Sensitivity Analysis: (22 points) You have rented a metal detector for two and a half hours. You can spend your time with
School: Stanford
Course: Finance For Non-MBAs
Review Session before Final Finance for non MBAs TA: Pablo Villanueva (using previous TAs notes) pvillanueva@stanford.edu December 11th, 2011 Agenda for Today 1. Practice Questions Binomial Options Pricing. Capital Structure. Currency Hedging. Put-Cal
School: Stanford
Lecture Notes in Macroeconomics John C. Driscoll Brown University and NBER1 December 3, 2001 Department of Economics, Brown University, Box B, Providence RI 02912. Phone (401) 863-1584, Fax (401) 863-1970, email:John Driscoll@brown.edu, web:http:\ c
School: Stanford
Course: PROGRAMMING METHODOLOGY
Project 3 Mini-Contest Long Names some team no one cares about, and in rst place we have Stay Duck which has won the compeDDon and is the best bot ever programmed that I have seen in my life! (read in Abbeel's
School: Stanford
Course: PROGRAMMING METHODOLOGY
Project 2 Mini-Contest Fun Names out of Top-10 It is baseline Team, just have a look, no Ame to do it. There will be midterms nextweek L tooskoolforkool became notverysmart I Raise Your ELO mkay Top-10
School: Stanford
Course: PROGRAMMING METHODOLOGY
Project 1 Mini-Contest Fun Names in 11-31 ^_^ Alex Sawai L)cfw_ :|:&;: Craig Hiller and Chris Hsu noeort Jason McGhee AmingForLast Jinyu Oh [15] Top-10 10 TuPAC Kasey Moat 9 test111 Boyi
School: Stanford
Course: PROGRAMMING METHODOLOGY
CS188 Spring 2014 Section 11: Machine Learning You want to predict if movies will be protable based on their screenplays. You hire two critics A and B to read a script you have and rate it on a scale of 1 to 5. The critics are not perfect; here are ve dat
School: Stanford
Course: Decision Analysis Applications: Business Strategy And Public Policy
School: Stanford
Course: On Achievability Via Random Binning
1 On Achievability via Random Binning Ritesh Kolte, Kartik Venkat cfw_rkolte, kvenkat@stanford.edu AbstractIn [1], the authors present a novel tool to establish achievability results in network information theoretic problems. The main idea is to study a s
School: Stanford
Week 8 Discussion 1 "Inspirational and Relational Leadership Perspectives" Please respond to the following: From the first e-Activity, evaluate whether the decision to expose the companys culture, strategy, and secrets to outsiders was or was not a good i
School: Stanford
Course: Introductory Economics A
Li 1 Report on P&G Company Table of Content Li 2 1. Executive Summary This report mainly aims at providing an analysis of P&G Company. The P&G Company is one of the worlds most well-known companies of consumer goods. The components of the report include:
School: Stanford
Brendon Pezzack January 18, 2007 Bio 44x Lab Protocal for Enzyme Lab Identifying unknown regulators by their affects on B-galactosidase activity using an in vivo assay of Escherichia coli and an in vitro assay of purified B-galactosidase from Aspergillus
School: Stanford
Course: Introduction To Philosophy
Cho 1 In support of inference to the best explanation and its application in discrediting skepticism Some philosophers claim that the absence of decisive or conclusive evidence against theories postulating that we are brains in vats (referred to as the B
School: Stanford
Course: Analog Integrated Circuit Design
Lecture 6 Design Example 2 Extrinsic Capacitance Boris Murmann Stanford University murmann@stanford.edu Copyright 2004 by Boris Murmann B. Murmann EE 214 Lecture 6 (HO#9) 1 Overview Reading 1.6.7 (Parasitic Elements) 7.1, 7.2.0, 7.2.1 (Mille
School: Stanford
Course: Introduction To Time Series Analysis
Examples of Time Series Data Models for Time Series Data Lecture 01, Stat 207, Spring 2012 Examples of Time Series Professor David Donoho 2012-04-03 Professor David Donoho Lecture 01, Stat 207, Spring 2012 Examples of Time Series Data Models for Time Seri
School: Stanford
Course: Economic Analysis II
Professor Jay Bhattacharya Spring 2001 Preview of Rest of the Course We have covered consumer decision making and firm decision making. One more lecture on decision making under uncertainty-next Tuesday. The rest of the lectures will focus on how consume
School: Stanford
Course: INTRODUCTION TO OPTIMIZATION
MS&E111 Introduction to Optimization Prof. Amin Saberi Lecture 8 May 1-3, 2006 1 Two player Zero-Sum games In this section, we consider games in which each of two opponents selects a strategy and receives a payoff contingent on both his own and his oppone
School: Stanford
Course: Analog Integrated Circuit Design
Lecture 24 kT/C Noise Boris Murmann Stanford University murmann@stanford.edu Copyright 2004 by Boris Murmann B. Murmann EE 214 Lecture 24 (HO#32) 1 Overview Introduction Having established the basic noise mechanisms in MOSFETS, today's lectur
School: Stanford
Course: The Fourier Transform And Its Applications
EE261 Raj Bhatnagar Summer 2009-2010 EE 261 The Fourier Transform and its Applications Midterm Examination 19 July 2010 (a) This exam consists of 4 questions with 12 total subparts for a total of 50 points. (b) The questions dier in length and diculty. Do
School: Stanford
Course: Basic Physics For Solid State Electronics
1. Semiconductor carrier statistics (40 points) Consider a semiconductor with a face-centered cubic lattice and with cubic symmetry. The valence band has a maximum at with an energy E = 0 and with an effective mass m0 = me. (me is the mass of a free elect
School: Stanford
EE263 Dec. 56 or Dec. 67, 2008. Prof. S. Boyd Final exam This is a 24 hour take-home nal exam. Please turn it in at Bytes Cafe in the Packard building, 24 hours after you pick it up. Please read the following instructions carefully. You may use any books,
School: Stanford
Course: Dynamic Systems
MS&E 201 Dynamic Systems Spring 04-05 Final Exam, Page 1 of 8 June 8, 2005 Professor Edison Tse MS&E 201 DYNAMIC SYSTEMS FINAL EXAM 2004-2005 THREE HOURS 180 minutes, total 100 points. Open Book. Open Notes. Write your name on this page of the exam. You w
School: Stanford
CS 276: Information Retrieval and Web Search Open Book Final Examination This examination consists of 16 pages, 10 questions, and 100 points and counts for 30 percent of your final grade. Please write your answers on the exam paper in the spaces provided.
School: Stanford
x h s w e p x h s s u w j n h x h n h x j x o x x o h h m m o h x x x r x x x j i x o j n x k x f f u i i w g i u o n x x x x i n x h o x x j o f j i w i j x x k m j k k x n m j j h x p h x f n r x n n i m n n q q w f j h k o f j w l n n k f j o j m x i
School: Stanford
Course: Data Analysis
STATS 202 Homework 1 Hao Chen July 3, 2011 In total: 40 points. Problem 2 (26 points, 2 points each) Classify the following attributes as binary, discrete, or continuous. Also classify them as qualitative (nominal or ordinal) or quantitative (interval or
School: Stanford
Course: Modern Applied Statistics: Learning
STATS 315A Winter 2007 Homework 1 Solutions Prob. #1 (Thanks to Wei Zhen) (a) The function mixG takes a centroid matrix mu, a vector N specifying the number of samples in each group and the noise variance v. mixG <- function (mu, N, v)cfw_ mu <- rbind(mu)
School: Stanford
Course: LINEAR AND NON-LINEAR OPTIMIZATION
MS&E 211 Linear & Nonlinear Optimization Fall 2011 Prof Yinyu Ye Homework Assignment 3: Due Tuesday November 1st at 6:00pm Problem 1. Sensitivity Analysis: (22 points) You have rented a metal detector for two and a half hours. You can spend your time with
School: Stanford
Course: ECONOMIC GROWTH AND DEVELOPMENT
Economic Growth and Development Professor Olivier de La Grandville Problem Set 1 To be returned Friday, Oct 3rd , 2008 MS&E 249 Fall 2008 1. In his classic paper, Robert Solow gives the solution of the differential equation for r, corres sponding to the W
School: Stanford
CS161 Summer 2013 Handout 09S July 31, 2013 Problem Set 4 Solutions Problem One: Insertion Sort Revisited (4 Points) Theorem: E[I] = (n2). Proof: For any pair of positions 1 i < j n, let Cij be an indicator random variable that is 1 if the elements at pos
School: Stanford
Course: DATA STRUCTURES
E40 / Spring 2012 LAB. 1B: SERIAL AND PARALLEL CONNECTIONS The design portion of the prelab is to be done with your lab partner. Each group of two students need to turn in only one report. OBJECTIVES To examine the current, voltage and power characteristi
School: Stanford
Brendon Pezzack Tuesday Group A Partner: Cayde Ritchie Bio 44X Lab Report: Molecular Biology Examining the effects of various trpR (gene) missense mutations on TrpR protein functionality in Escherichia coli using trp/lac operon fusion, X-gal assays, ligat
School: Stanford
Course: Modern Statistical Learning
Multivariatedensityestimation &MachineLearning WingHungWong StanfordUniversity Example:multiparameterflowcytometrydata TheBayesiannonparametricproblem x1, x2, xn areindependentr.v.onaspace TheirdistributionQisunknownbutassumedtobe drawnfromapriordistrib
School: Stanford
Course: Modern Statistical Learning
Stats 270/370 Homework 6 Due Wednesday, March 19th Problem Let be the state space, and let T = (T0 , T1 , ) be a sequence of nested bifurcating partitions of . That is T0 = cfw_ T1 = cfw_0 , 1 T2 = cfw_00 , 01 , 10 , 11 Tk = cfw_ : cfw_0, 1k where = (
School: Stanford
Course: Modern Statistical Learning
Stats 270/370 Homework 5 Due Wednesday, March 12th Problem Assume that the parameter space is a bounded interval in R1 . Let (; x) be a function satisfying the properties that E (, X) = p (; x)dx = 0 has a unique solution at = . Consider the following est
School: Stanford
Course: Modern Statistical Learning
Stats 270/370 Homework 4 Due Wednesday, Feb. 26th Problem 1 Let X1 , X2 , , Xn be i.i.d. N (, 2 ) with unknown and 2 . Now let A (X) be the usual 1 condence interval for . That is, s A (X) = cfw_ : |X | t/2 (n 1) , n where t/2 (n 1) is the 1 /2 quantile o
School: Stanford
Course: Modern Statistical Learning
Stats 270/370 Homework 1 Due Wednesday Jan. 22nd In Class Problem 1 Suppose X1 , , Xn are iid Poisson(). Show by direct calculation without using any theorem in mathematical statistics, that (a) X = n i=1 Xi /n is an unbiased estimator for . (b) X is opti
School: Stanford
Course: INVESTMENT SCIENCE
MS&E 242 Investment Science Professor: Class Location and Time: Class Description: Enrollment: Web page: Course email: Course Assistants: Staff Assistant: Required Course Text:Investment Science Prerequisites: Honor Code: Homework: 5pm418 Terman or i
School: Stanford
Course: Computer Organization And Systems
CS107 Handout 02 January 9th, 2012 Winter 2012 CS107 Course Outline Rough Outline of What To Expect Introduction to Systems, Lower-level C Constructs o C basics, UNIX development tools, gcc, gdb. o C pointers and arrays, C strings, malloc, realloc, and fr
School: Stanford
Course: Advanced Analog Integrated Circuit Design
EE214B Winter 11-12 D. Allstot Handout #1 Page 1 of 4 STANFORD UNIVERSITY Department of Electrical Engineering EE214B: Advanced Analog Integrated Circuit Design http:/ccnet.stanford.edu/ee214B/ TIME: Class: MWF 11:00-11:50 AM, Thornton 102 Review Session:
School: Stanford
Course: 51
Economics 1A The First Part of a Two Part Sequence in Introductory Economics Stanford University Department of Economics Fall Quarter 2009-2010 John B. Taylor 248 Landau Building, 723-9677 JohnBTaylor@Stanford.Edu Office hours: Mon: 2-3:30, Wed: 11-12 Int
School: Stanford
Course: Chemical Principles I
CME100 Vector Calculus for Engineers V. Khayms Fall 2011 Course Information Sheet Instructor: Vadim Khayms (vadim@stanford.edu) Office hours: Tue. 6:00-8:00pm Phone: (408) 203-0822 TAs: Michael Lesnick (mlesnick@stanford.edu) Ajith Morpathi (ajithm@stanfo
School: Stanford
Course: NUMERICAL LINEAR ALGEBRA
Numerical Linear Algebra M. Gerritsen Autumn 2009 General Information CME 302 provides in-depth knowledge of matrix computations. It covers extensively algorithms for eigenvalue and eigenvector computations, the singular value decomposition and iterative
School: Stanford
Course: The Fourier Transform And Its Applications
EE261 Raj Bhatnagar Summer 2009-2010 EE 261 The Fourier Transform and its Applications Midterm Examination 19 July 2010 (a) This exam consists of 4 questions with 12 total subparts for a total of 50 points. (b) The questions dier in length and diculty. Do
School: Stanford
x h s w e p x h s s u w j n h x h n h x j x o x x o h h m m o h x x x r x x x j i x o j n x k x f f u i i w g i u o n x x x x i n x h o x x j o f j i w i j x x k m j k k x n m j j h x p h x f n r x n n i m n n q q w f j h k o f j w l n n k f j o j m x i
School: Stanford
Course: INVESTMENT SCIENCE
Lecture #9 Markowitz Portfolio Theory Reading: Luenberger Chapter 6, Sections 6 - 10 Primbs/Investment Science 1 The Markowitz Model Solving the Optimization The Two Fund Theorem Markowitz Portfolio Theory Inclusion of a Risk Free Asset The One Fu
School: Stanford
Course: Data Analysis
STATS 202 Homework 1 Hao Chen July 3, 2011 In total: 40 points. Problem 2 (26 points, 2 points each) Classify the following attributes as binary, discrete, or continuous. Also classify them as qualitative (nominal or ordinal) or quantitative (interval or
School: Stanford
Course: Modern Applied Statistics: Learning
STATS 315A Winter 2007 Homework 1 Solutions Prob. #1 (Thanks to Wei Zhen) (a) The function mixG takes a centroid matrix mu, a vector N specifying the number of samples in each group and the noise variance v. mixG <- function (mu, N, v)cfw_ mu <- rbind(mu)
School: Stanford
Course: LINEAR AND NON-LINEAR OPTIMIZATION
MS&E 211 Linear & Nonlinear Optimization Fall 2011 Prof Yinyu Ye Homework Assignment 3: Due Tuesday November 1st at 6:00pm Problem 1. Sensitivity Analysis: (22 points) You have rented a metal detector for two and a half hours. You can spend your time with
School: Stanford
1 CS229 Problem Set #2 Solutions CS 229, Autumn 2011 Problem Set #2 Solutions: and Theory Naive Bayes, SVMs, Due in class (9:30am) on Wednesday, November 2. Notes: (1) These questions require thought, but do not require long answers. Please be as concise
School: Stanford
Course: ECONOMIC GROWTH AND DEVELOPMENT
Economic Growth and Development Professor Olivier de La Grandville Problem Set 1 To be returned Friday, Oct 3rd , 2008 MS&E 249 Fall 2008 1. In his classic paper, Robert Solow gives the solution of the differential equation for r, corres sponding to the W
School: Stanford
CS161 Summer 2013 Handout 09S July 31, 2013 Problem Set 4 Solutions Problem One: Insertion Sort Revisited (4 Points) Theorem: E[I] = (n2). Proof: For any pair of positions 1 i < j n, let Cij be an indicator random variable that is 1 if the elements at pos
School: Stanford
Course: Basic Physics For Solid State Electronics
1. Semiconductor carrier statistics (40 points) Consider a semiconductor with a face-centered cubic lattice and with cubic symmetry. The valence band has a maximum at with an energy E = 0 and with an effective mass m0 = me. (me is the mass of a free elect
School: Stanford
CS229 Problem Set #3 1 CS 229, Autumn 2011 Problem Set #3: Theory & Unsupervised learning Due in class (9:30am) on Wednesday, November 16. Notes: (1) These questions require thought, but do not require long answers. Please be as concise as possible. (2) W
School: Stanford
Course: Theory Of Probability
Stat 116 Homework 3 Due Wednesday, April 23th. Please show work and justify answers. No credit for a nal answer with no explanation, even if the answer is correct. 1. Airlines nd that each passenger who books a ight fails to check-in with probability 1 in
School: Stanford
Course: LINEAR AND NON-LINEAR OPTIMIZATION
MS&E 211 Linear & Nonlinear Optimization Fall 2011 Prof Yinyu Ye Homework Assignment 3: SOLUTIONS Problem 1. Sensitivity Analysis: (22 points) [2 points each] You have rented a metal detector for two and a half hours. You can spend your time with it searc
School: Stanford
Course: Stochastic Modeling
MS&E 221 Ramesh Johari Problem Set 2 Due: February 7, 2007, 5:00 PM outside Terman 319 Reading. Same as last week: read Section 4.4 in Ross. Problem 1. Bertsekas and Tsitsiklis, Chapter 6, Problem 11 (note that steady state means the current distribution
School: Stanford
1 CS229 Problem Set #1 CS 229, Autumn 2011 Problem Set #1: Supervised Learning Due in class (9:30am) on Wednesday, October 19. Notes: (1) These questions require thought, but do not require long answers. Please be as concise as possible. (2) When sending
School: Stanford
Course: Introduction To Time Series Analysis
Stats 207 Homework 3 Solution May 2, 2012 1. (a) Trough simple recursion we have xt = t 1 i=0 Thus t 1 E (x t ) = i=0 i t i . E(i ti ) = 0, and Var(xt ) = t 1 i=0 2 Var(i ti ) = t 1 2 i = i=0 2 (1 2t ) . 1 2 Thus xt is not stationary because its varian
School: Stanford
CS229 Problem Set #3 Solutions 1 CS 229, Autumn 2011 Problem Set #3 Solutions: Theory & Unsupervised learning Due in class (9:30am) on Wednesday, November 16. Notes: (1) These questions require thought, but do not require long answers. Please be as concis
School: Stanford
Course: MODERN APPLIED STATISTICS: LEARNING
HW 3 Solutions March 18, 2013 Grade distribution: Problems 1 - 5: 12 points each, Problem 6: 15 points for writeup, 15 points for computation. Problem 1 a) Let cfw_X, y denote the full original dataset, and let cfw_X(i) , y(i) denote the dataset with the
School: Stanford
CS161 Summer 2013 Handout 09 July 22, 2013 Problem Set 4 This problem set is all about randomness randomized algorithms, randomized data structures, random variables, etc. By the time you're done with this problem set, we hope that you have a much more nu
School: Stanford
Course: EE314
EE214B Feedback Circuits Part II Handout #9 B. Murmann Stanford University Winter 2012-13 Textbook Sections: 5.4, 9.4.4 Motivating Example: TIA for High-Speed Optical Networks Transimpedance gain 1800 Bandwidth 34 GHz Input noise 25 Maximum input 1.3 mAp
School: Stanford
Course: Digital MOS Integrated Circuits
EE313 Winter 2009-10 J. Kim & M. Horowitz Handout #8 page 1 of 10 SOLUTIONS TO HOMEWORK #0 Problem # 1 (1.1) Run HSPICE on etude1.sp. (1.2) Use CScope to look at the DC transfer characteristic curves. Notice that inverters with different ratios have diffe
School: Stanford
1 CS229 Problem Set #4 CS 229, Autumn 2011 Problem Set #4: Unsupervised learning & RL Due in class (9:30am) on Wednesday, December 7. Notes: (1) These questions require thought, but do not require long answers. Please be as concise as possible. (2) When s
School: Stanford
Course: Stochastic
MS&E 221 Ramesh Johari Problem Set 2 Due: Weds., February 2, 2011, 5:00 PM in the basement of HEC Reading. 4.1-4.4, 4.5.1, 4.7 in Ross. Problem 1. Ross, Chapter 4, problem 14: Problem 2. (A queueing model) Consider a queue (or a waiting room) that can hol
School: Stanford
EE263 Dec. 56 or Dec. 67, 2008. Prof. S. Boyd Final exam This is a 24 hour take-home nal exam. Please turn it in at Bytes Cafe in the Packard building, 24 hours after you pick it up. Please read the following instructions carefully. You may use any books,
School: Stanford
Course: ARTIFICIAL INTELLIGENCE: PRINCIPLES AND TECHNIQUES
CS221 Problem Set #1 1 CS 221 Problem Set #1: Search, Motion Planning, CSPs Due by 9:30am on Tuesday, October 13. Please see the course information page on the class website for late homework submission instructions. SCPD students can also fax their solut
School: Stanford
Course: INVESTMENT SCIENCE
Lecture #10 The Capital Asset Pricing Model (CAPM 1) Reading: Luenberger Chapter 7, Sections 1 4 Primbs/Investment Science 1 Market Equilibrium CAPM The Capital Asset Pricing Model Consequences of CAPM Primbs/Investment Science 2 Market Equili
School: Stanford
Course: INVESTMENT SCIENCE
Lecture 3: Fixed Income Securities Reading: Luenberger Chapter 3, Sections 1- 4 Primbs/Investment Science 1 Definitions Annuity Formulas Fixed Income Securities Bond Details Bond Price and Yield Primbs/Investment Science 2 Definitions Financial
School: Stanford
MS&E 261 Winter 2010-11 Prof. Warren H. Hausman SOLUTIONS TO PROBLEM SET #4 1. a) and b) Forecast (86 + 75)/2 (75 + 72)/2 etc = = 80.5 73.5 77.5 107.5 98.5 87.5 100.0 78.5 79.5 95.0 = = Period 3 4 5 6 7 8 9 10 11 12 21.6 717.5 Actual 72 83 132 65 110 90 6
School: Stanford
Course: Probabilistic Graphical Models: Principles And Techniques
CS228 Problem Set #1 1 CS 228, Winter 2011-2012 Problem Set #1 This assignment is due at 12 noon on January 23. Submissions should be placed in the filing cabinet labeled "CS228 Homework Submission Box" located in the lobby outside Gates 187" A Hidden Mar
School: Stanford
Course: Dynamic Systems
MS&E 201 Dynamic Systems Spring 04-05 Final Exam, Page 1 of 8 June 8, 2005 Professor Edison Tse MS&E 201 DYNAMIC SYSTEMS FINAL EXAM 2004-2005 THREE HOURS 180 minutes, total 100 points. Open Book. Open Notes. Write your name on this page of the exam. You w
School: Stanford
CS 276: Information Retrieval and Web Search Open Book Final Examination This examination consists of 16 pages, 10 questions, and 100 points and counts for 30 percent of your final grade. Please write your answers on the exam paper in the spaces provided.
School: Stanford
Introduction to Stochastic Processes Stat217, Summer 2012 Homework 4 - due in class on Tuesday July 31, 2012 PK = Pinsky and Karlin, Introduction to Stochastic Modeling, 4th edition (or TK = Taylor and Karlin, Introduction to Stochastic Modeling, 3rd edit
School: Stanford
Course: Information Theory
EE376A: Homeworks #6 Solutions 1. Cascaded BSCs. Consider the two discrete memoryless channels (X , p1 (y |x), Y ) and (Y , p2 (z |y ), Z ). Let p1 (y |x) and p2 (z |y ) be binary symmetric channels with crossover probabilities 1 and 2 respectively. 1 1 0
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278B Statistical Signal Processing October 20, 2011 Handout #6 Homework #4 Due Thursday, October 27 1. Coloring and whitening. Let 210 = 1 2 1 . 012 a. Find the coloring and whitening matrices of using the eigenvalue method discussed in lecture slides
School: Stanford
Course: Accounting
CHAPTER3 COVERAGEOFLEARNING OBJECTIVES LEARNING OBJECTIVES LO1: Use double-entry accounting. LO2: Describe the five steps in the recording process. LO3: Analyze and journalize transactions and post journal entries to the ledgers. LO4: Prepare and use a tr
School: Stanford
MS&E 261 Winter 2010-11 Prof. Warren H. Hausman SOLUTIONS TO PROBLEM SET #1 1. c K I a) b) c) d) 229 3 wks = = = = 280 2.40 45 .20 Q* = T = Q*/ = 229/280 = .8179 yrs. (= 9.81 months) G* = = = $109.98 R 9.82 mos. r = = (280)(3/52) = 16.15 r = 16 units 2. S
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278B Statistical Signal Processing October 29, 2011 Handout #9 Homework #4 Solutions 1. (10 points) Coloring and whitening. a. We denote the eigenvalue and eigenvector matrices of as and U , respectively. After using linear algebra methods (or Matlab,
School: Stanford
Course: Introduction To Time Series Analysis
STATS 207 Assignment 2 Solution April 30, 2012 1(1.10) a) M SA(A) = E [(xt+l Axt )2 ] = E [x2+l ] 2AE [xt+l xt ] + A2 E [x2 ] t t = (0) 2A (l) + A2 (0) 2 Here, A2 M SE (A) = 2 (0) > 0 and thus MSA(A) is minimized when A satises M SE (A) = 2 (0) 2A (h) = 0
School: Stanford
Course: Probabilistic Graphical Models: Principles And Techniques
CS228 Problem Set #4 1 CS 228, Winter 2008 Problem Set #4 1. Parameter Estimation in Template-Based Models [20 points] In class, we talked about parameter learning in the case of partially observed data for general Bayesian networks. Here, we apply these
School: Stanford
Course: SUPPLY CHAIN MANAGEMENT
Rev. 3/05 Information Distortion And the Bullwhip Effect Hau L. Lee Stanford University (used with permission) Hau Lee / Warren Hausman Page No. 1 Increasing Variability of Orders Up the Supply Chain Consumer Sales 20 Order 15 Quantity 10 5 0 Time Wholesa
School: Stanford
Course: Programming Paradigms
CS107 J Zelenski Handout #5 Oct 23, 2009 Midterm practice Midterm Exam: Friday, Oct 30 11am-12:15pm Location TBA The midterm exam is next Friday in-class. Open book/notes You may bring your textbooks, notes, handouts, code printouts, etc. to refer to duri
School: Stanford
Course: Organic Monofunctional Compounds
Chemistry 35: Organic Monofunctional Compounds Prof. Huestis, Fall 2008 TA: Leila Yeh Beach AMINES Part I: Summary of Amine Reactions Hofmann rearrangement H N R imines the carbonyl carbon is decarboxylated and the amine is transfered to its -carbon
School: Stanford
Course: Data Analysis
Homework 5 Solutions 1) Read Chapter 5 (Sections 5.2, 5.3, 5.5 and 5.6). 2) a) library(class) train<-read.csv("sonar_train.csv",header=FALSE) y<-as.factor(train[,61]) x<-train[,1:60] test<-read.csv("sonar_test.csv",header=FALSE) y_test<-as.factor(test[,61
School: Stanford
Course: Mathematical Methods For Computer Vision, Robotics, And Graphics
CS205 Homework #8 Solutions Problem 1 Give a criterion for the well-posedness of the kth order, scalar, homogeneous, constant-coefficient ODE u(k) + ck-1 u(k-1) + + c1 u + c0 u = 0 (Hint: Transform to a first-order system y = Ay and observe A is
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #11 10/26/2007 Homework Assignment #5 Due on Thursday November 1st, 11:59 pm In this assignment you are required to turn in the probabilistic section only. Again the "Food for thought" section is optional but will help
School: Stanford
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2012 Problem Set Nine Due Friday, December 7 1. (20 points) 2D Fourier Transforms Find the 2D Fourier Transforms of: (a) sin 2ax1 sin 2bx2 Solution: Because the function is separable we have F (sin 2a
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278B Statistical Signal Processing Tuesday, December 6, 2011 Handout #19 Homework #7 Solutions 1. (20 points) Autocorrelation functions. a. The mean function is X (t) = E[At + B ] = E[A]t + E[B ] = 0. The autocorrelation function is RX (t1 , t2 ) = E[(
School: Stanford
Course: DECISION ANALYSIS II
MS&E 352 Handout #6 Decision Analysis II January 29th, 2009 _ Problem Set #2 Due Thursday February 5th Part I Advanced Information Gathering Problem 1 Alpha / Beta Detectors [35 points] Kim faces the Party Problem in Professor Howard's manuscript. Two sal
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 November 29th, 2001 Handout #25, page 1 of 25 SAMPLE FINAL: EES&OR 252 Final Examination (1997-1998) Please do not begin the exam until you are instructed to do so. Name (printed clearly): _ 1. Count the number of pages in this exam. There should
School: Stanford
Course: SUPPLY CHAIN MANAGEMENT
Rev 3/08 National Bicycle Industrial Co. National Bicycle Industrial Co. Transport Bike Mkt Position Sales Trend Variety Cost Profit Uncertainty Sport Bike The National Bicycle Supply Chain The National Bicycle Supply Chain Suppliers Factory Hansha WHs Re
School: Stanford
Course: Structural Probability Model
CS228 Programming Assignment#1 1 CS 228, Winter 2009-2010 Programming Assignment #1Inference in Graphical Models In this assignment you will implement the Sum Product Message Passing algorithm for exact inference in Graphical Models, and then extend the a
School: Stanford
Course: The Fourier Transform And Its Applications
EE 261 The Fourier Transform and its Applications Fall 2009 Solutions to Problem Set One 1. Some practice with geometric sums and complex exponentials (5 points each) Well make much use of formulas for the sum of a geometric series, especially in combinat
School: Stanford
Course: INVESTMENT SCIENCE
MS&E 242 Investment Science Professor: Class Location and Time: Class Description: Enrollment: Web page: Course email: Course Assistants: Staff Assistant: Required Course Text:Investment Science Prerequisites: Honor Code: Homework: 5pm418 Terman or i
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Course: INVESTMENT SCIENCE
Lecture 4: The Term Structure of Interest Rates Reading: Luenberger Chapter 4, Sections 1 5 Primbs/Investment Science 1 The yield curve Spot and forward rates The Term Structure of Interest Rates Term structure explanations Expectation dynamics
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Course: Data Analysis
Homework 5 - Stats 202 Page 1 of 2 Homework 5 1) Read Chapter 5 (Sections 5.2, 5.3, 5.5 and 5.6). 2) This question deals with In Class Exercise #34. a) Repeat In Class Exercise #34 for the k-nearest neighbor classifier for k=1,2,.,10. (We did k=1 in class
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1 CS229 Practice Midterm Solutions CS 229, Autumn 2011 Practice Midterm Solutions Notes: 1. The midterm will have about 5-6 long questions, and about 8-10 short questions. Space will be provided on the actual midterm for you to write your answers. 2. The
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1 CS229 Problem Set #1 CS 229, Autumn 2011 Problem Set #1 Solutions: Supervised Learning Due in class (9:30am) on Wednesday, October 19. Notes: (1) These questions require thought, but do not require long answers. Please be as concise as possible. (2) Whe
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Course: Introduction To Statistical Signal Processing
EE 278 Statistical Signal Processing Homework #8 Due: Wednesday, December 2 November 18, 2009 Handout #18 1. Discrete-time Wiener process. Let cfw_Zn : n 0 be a discrete-time white Gaussian noise process; that is, Z1 , Z2 , Z3 , . . . are i.i.d. N (0, 1).
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Course: Finance For Non-MBAs
Review Session before Final Finance for non MBAs TA: Pablo Villanueva (using previous TAs notes) pvillanueva@stanford.edu December 11th, 2011 Agenda for Today 1. Practice Questions Binomial Options Pricing. Capital Structure. Currency Hedging. Put-Cal
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Lecture Notes in Macroeconomics John C. Driscoll Brown University and NBER1 December 3, 2001 Department of Economics, Brown University, Box B, Providence RI 02912. Phone (401) 863-1584, Fax (401) 863-1970, email:John Driscoll@brown.edu, web:http:\ c
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Course: PROGRAMMING METHODOLOGY
Project 3 Mini-Contest Long Names some team no one cares about, and in rst place we have Stay Duck which has won the compeDDon and is the best bot ever programmed that I have seen in my life! (read in Abbeel's
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Course: PROGRAMMING METHODOLOGY
Project 2 Mini-Contest Fun Names out of Top-10 It is baseline Team, just have a look, no Ame to do it. There will be midterms nextweek L tooskoolforkool became notverysmart I Raise Your ELO mkay Top-10
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Course: PROGRAMMING METHODOLOGY
Project 1 Mini-Contest Fun Names in 11-31 ^_^ Alex Sawai L)cfw_ :|:&;: Craig Hiller and Chris Hsu noeort Jason McGhee AmingForLast Jinyu Oh [15] Top-10 10 TuPAC Kasey Moat 9 test111 Boyi
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Course: PROGRAMMING METHODOLOGY
CS188 Spring 2014 Section 11: Machine Learning You want to predict if movies will be protable based on their screenplays. You hire two critics A and B to read a script you have and rate it on a scale of 1 to 5. The critics are not perfect; here are ve dat
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Course: PROGRAMMING METHODOLOGY
CS188 Spring 2014 Section 10: ML Pacman and Mrs. Pacman have been searching for each other in the Maze. Mrs. Pacman has been pregnant with a baby, and just this morning she has given birth to Pacbaby (Congratulations, Pacmans!). Because Pacbaby was born b
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Course: PROGRAMMING METHODOLOGY
CS 188 Spring 2014 Introduction to Articial Intelligence Section Handout 9 Value Of Information Used Car Purchase [Adapted from problem 16.11 in Russell & Norvig] A used car buyer can decide to carry out various tests with various costs (e.g., kick the ti
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Course: PROGRAMMING METHODOLOGY
CS188 Spring 2014 Section 7: Probability and Bayes Nets 1 Green Party President Its election year again! In a parallel universe the Green Party is running for presidency. Pundits believe that Green Party presidents are more likely to legalize marijuana th
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Course: PROGRAMMING METHODOLOGY
CS188 Spring 2014 Section 8: Inference and Sampling 1 Sampling and Dynamic Bayes Nets Many people would prefer to eat ice cream on a sunny day than on a rainy day. We can model this situation with a Bayesian network. Suppose we consider the weather, along
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Course: PROGRAMMING METHODOLOGY
CS188 Spring 2014 Section 5: Reinforcement Learning 1 Learning with Feature-based Representations We would like to use a Q-learning agent for Pacman, but the state size for a large grid is too massive to hold in memory (just like at the end of Project 3).
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Course: PROGRAMMING METHODOLOGY
CS188 Spring 2014 Section 6: Hidden Markov Models 1 Basic computations Consider the following Hidden Markov Model. X1 X2 O1 O2 X1 0 1 Pr(X1 ) 0.3 0.7 Xt 0 0 1 1 Xt+1 0 1 0 1 Pr(Xt+1 |Xt ) 0.4 0.6 0.8 0.2 Xt 0 0 1 1 Ot A B A B Pr(Ot |Xt ) 0.9 0.1 0.5 0.5 S
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Course: PROGRAMMING METHODOLOGY
CS188 Spring 2014 Section 4: MDPs 1 MDPs: Micro-Blackjack In micro-blackjack, you repeatedly draw a card (with replacement) that is equally likely to be a 2, 3, or 4. You can either Draw or Stop if the total score of the cards you have drawn is less than
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Course: PROGRAMMING METHODOLOGY
CS188 Spring 2014 Section 3: Games 1 Nearly Zero Sum Games The standard Minimax algorithm calculates worst-case values in a zero-sum two player game, i.e. a game in which for all terminal states s, the utilities for players A (MAX) and B (MIN) obey UA (s)
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Course: PROGRAMMING METHODOLOGY
CS188 Spring 2014 Section 2: CSPs 1 Course Scheduling You are in charge of scheduling for computer science classes that meet Mondays, Wednesdays and Fridays. There are 5 classes that meet on these days and 3 professors who will be teaching these classes.
School: Stanford
Course: PROGRAMMING METHODOLOGY
CS188 Spring 2014 Section 1: Search 1 Search and Heuristics Imagine a car-like agent wishes to exit a maze like the one shown below: The agent is directional and at all times faces some direction d (N, S, E, W ). With a single action, the agent can either
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Course: PROGRAMMING METHODOLOGY
CS188: Articial Intelligence, Fall 2008 Written Assignment 1 CS188 Spring 2014September 11th at theSearchof lecture Section 0: beginning Due: 11 Search Search Strategies Graph algorithms in action A h=2 Goal 2 2 4 Start C h=2 5 5 1 3 B h=5 D h=1 4 Our int
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Course: PROGRAMMING METHODOLOGY
CS 188 Spring 2012 Introduction to Articial Intelligence Section 10 Solutions Naive Bayes and Perceptrons Q1. Credit Card Fraud Detection You are building a fraud detection system for a credit card company. They have the following records of purchases for
School: Stanford
Course: PROGRAMMING METHODOLOGY
CS 188 Spring 2012 Introduction to Articial Intelligence Section 10 Naive Bayes and Perceptrons Q1. Credit Card Fraud Detection You are building a fraud detection system for a credit card company. They have the following records of purchases for which the
School: Stanford
Course: PROGRAMMING METHODOLOGY
CS188 Spring 2012 Section 9: VPI 1 Used Car Purchase [Adapted from problem 16.11 in Russell & Norvig] A used car buyer can decide to carry out various investigations with various costs (e.g., kick the tires, take the car to a qualied mechanic) and then, d
School: Stanford
Course: PROGRAMMING METHODOLOGY
CS188 Spring 2012 Section 8: Particle Filters 1 Jabberwock in the wild Lewis Jabberwock is in the wild: its position is in a two-dimensional discrete grid, but this time the grid is not bounded. In other words, the position of the Jabberwock is a pair of
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Course: PROGRAMMING METHODOLOGY
CS188 Spring 2012 Section 9: VPI 1 Used Car Purchase [Adapted from problem 16.11 in Russell & Norvig] A used car buyer can decide to carry out various investigations with various costs (e.g., kick the tires, take the car to a qualied mechanic) and then, d
School: Stanford
Course: PROGRAMMING METHODOLOGY
CS188 Spring 2012 Section 8: Particle Filters 1 Jabberwock in the wild Lewis Jabberwock is in the wild: its position is in a two-dimensional discrete grid, but this time the grid is not bounded. In other words, the position of the Jabberwock is a pair of
School: Stanford
Course: PROGRAMMING METHODOLOGY
CS188 Fall 2012 Section 7: Probability and Bayes Nets 1 Green Party President Its election year again! In a parallel universe the Green Party is running for presidency. Pundits believe that Green Party presidents are more likely to legalize marijuana than
School: Stanford
Course: PROGRAMMING METHODOLOGY
CS188 Fall 2012 Section 7: Probability and Bayes Nets 1 Green Party President Its election year again! In a parallel universe the Green Party is running for presidency. Pundits believe that Green Party presidents are more likely to legalize marijuana than
School: Stanford
Course: PROGRAMMING METHODOLOGY
CS188 Fall 2012 Section 6: MDPs and RL 1 Treasure Hunting While Pacman is out collecting all the dots from mediumClassic, Ms. Pacman takes some time to go treasure hunting in the Gridworld island. Ever prepared, she has a map that shows where all the haza
School: Stanford
Course: PROGRAMMING METHODOLOGY
CS188 Spring 2012 Section 4: Games 1 Minimax Search In this problem, we will explore adversarial search. Consider the zero-sum game tree shown below. Trapezoids that point up, such as at the root, represent choices for the player seeking to maximize; trap
School: Stanford
Course: PROGRAMMING METHODOLOGY
CS188 Fall 2012 Section 6: MDPs and RL 1 Treasure Hunting While Pacman is out collecting all the dots from mediumClassic, Ms. Pacman takes some time to go treasure hunting in the Gridworld island. Ever prepared, she has a map that shows where all the haza
School: Stanford
Course: PROGRAMMING METHODOLOGY
CS188 Spring 2012 Section 4: Games 1 Minimax Search In this problem, we will explore adversarial search. Consider the zero-sum game tree shown below. Trapezoids that point up, such as at the root, represent choices for the player seeking to maximize; trap
School: Stanford
Course: PROGRAMMING METHODOLOGY
CS188 Spring 2012 Section 3: CSP 1 Mini-Sudoku Mini-Sudoku is a scaled-down variant of the popular game Sudoku. In mini-Sudoku, one is presented with a 4 4 grid, further partitioned into a 2 2 grid of 2 2 sub-grids, called regions (see the gure below). Ea
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Course: PROGRAMMING METHODOLOGY
CS188 Spring 2012 Section 3: CSP 1 Mini-Sudoku Mini-Sudoku is a scaled-down variant of the popular game Sudoku. In mini-Sudoku, one is presented with a 4 4 grid, further partitioned into a 2 2 grid of 2 2 sub-grids, called regions (see the gure below). Ea
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Course: PROGRAMMING METHODOLOGY
CS188 Spring 2012 Section 1: Search 1 n-Queens Max Friedrich William Bezzel invented the eight queens puzzle in 1848: place 8 queens on a chess board such that none of them can capture any other. The problem, and the generalized version with n queens, has
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Course: PROGRAMMING METHODOLOGY
CS188 Spring 2012 Section 1: Search 1 n-Queens Max Friedrich William Bezzel invented the eight queens puzzle in 1848: place 8 queens on a chess board such that none of them can capture any other. The problem, and the generalized version with n queens, has
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Course: PROGRAMMING METHODOLOGY
CS188 Spring 2012 Section 2: A* Search 1 Pancake Heuristics Here, we consider the pancake problem. A server is given a stack of n pancakes. Each pancake is a dierent size. The server can ip the top k pancakes, reversing their order. The cost of ipping k p
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Course: PROGRAMMING METHODOLOGY
CS188 Spring 2012 Section 2: A* Search 1 Pancake Heuristics Here, we consider the pancake problem. A server is given a stack of n pancakes. Each pancake is a dierent size. The server can ip the top k pancakes, reversing their order. The cost of ipping k p
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Course: Algorithms: Design And Analysis
Graph Primi*ves Design and Analysis of Algorithms I Introduc*on to Graph Search A Few Mo*va*ons 1. Check if a network is connected (can get to anywhere from anywhere else ) Bacon number = 2 2.
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Course: Algorithms: Design And Analysis
Graph Primi*ves Breadth-First Search Design and Analysis of Algorithms I Overview and Example Breadth-First Search (BFS) - explore nodes in layers - can compute shortest paths - connected components of und
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Course: Algorithms: Design And Analysis
Graph Primi*ves Depth-First Search Design and Analysis of Algorithms I Overview and Example Depth-First Search (DFS) : explore aggressively, only backtrack when necessary. - also computes a topological ordering of a
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Course: Algorithms: Design And Analysis
Graph Primi*ves Design and Analysis of Algorithms I An O(m+n) Algorithm for Compu*ng Strong Components Strongly Connected Components Formal Deni*on : the strongly connected components (SCCs) of a directed graph G a
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Course: Algorithms: Design And Analysis
Graph Primi*ves Structure of the Web Design and Analysis of Algorithms I The Web graph -Ver*ces = Web pages - (directed) edges = hyperlinks Ques*on : what does the web graph look like ? A co- aut
School: Stanford
Course: Algorithms: Design And Analysis
Design and Analysis of Algorithms I Lecture Notes on Strongly Connected Components1 The strongly connected components (SCCs) of a directed graph G are the equivalence classes of the following equivalence relation: u v if and only if there is a directed u
School: Stanford
Course: Algorithms: Design And Analysis
Graph Primi*ves Design and Analysis of Algorithms I Correctness of Kosarajus Algorithm Example Recap Tim Roughgarden Observa*on Claim : the SCCs of a directed graph G induce an acyclic meta-graph: - meta-nodes
School: Stanford
Course: Algorithms: Design And Analysis
Linear-Time Selec.on Randomized Selec.on (Analysis) Design and Analysis of Algorithms I Running Time of RSelect Rselect Theorem : for every input array of length n, the average running .me of Rselect is O(n) -
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Course: Algorithms: Design And Analysis
Linear-Time Selec.on Determinis.c Selec.on (Analysis) Design and Analysis of Algorithms I The DSelect Algorithm Choose Pivot DSelect(array A, length n, order sta.s.c i) 1. Break A into groups of 5, sort each group
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Course: Algorithms: Design And Analysis
Linear-Time Selec.on Randomized Selec.on (Algorithm) Design and Analysis of Algorithms I Prerequisites Watch this aBer: QuickSort - Par.oning around a pivot QuickSort Choosing a good pivot Probability Review, Par
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Course: Algorithms: Design And Analysis
Linear-Time Selec.on Determinis.c Selec.on (Algorithm) Design and Analysis of Algorithms I The Problem For simplicity Input : array A with n dis.nct numbers and a number Output : ith order sta.s.c (i.e., ith
School: Stanford
Course: Algorithms: Design And Analysis
Graph Algorithms Design and Analysis of Algorithms I Represen2ng Graphs Graphs Two ingredients Vertices aka nodes (V) Edges (E) = pairs of vertices can be undirected [unordered pair] or directed [ordered pair] (aka arcs) Examples: roa
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Course: Algorithms: Design And Analysis
Linear-Time Selec.on Design and Analysis of Algorithms I A Sor.ng Lower Bound Theorem : every comparison-based sor.ng algorithm has worst- case running .me [ assume determinis.c, but lower bound extends to randomized ]
School: Stanford
Course: Algorithms: Design And Analysis
Contrac(on Algorithm The Algorithm Design and Analysis of Algorithms I The Minimum Cut Problem INPUT: An undirected graph G = ( V, E ). [ Parallel edges allowed] [See other video for representation of the input] GOAL: Compute a cut w
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Course: Algorithms: Design And Analysis
Contrac(on Algorithm The Analysis Design and Analysis of Algorithms I The Minimum Cut Problem Input: An undirected graph G = (V, E). [parallel edges allowed] [See other video for representation of input] Goal: Compute a cut with fewest
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Course: Algorithms: Design And Analysis
Contrac(on Algorithm Overview Design and Analysis of Algorithms I Goals for These Lectures Further prac(ce with randomized algorithms In a new applica(on domain (graphs) Introduc(on to graphs and graph algorithms
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Course: Algorithms: Design And Analysis
Linear-Time Selec.on Determinis.c Selec.on (Analysis II) Design and Analysis of Algorithms I Rough Recurrence (Revisited) Let T(n) = maximum running .me of Dselect on an input array of length n. There is a constan
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Course: Algorithms: Design And Analysis
Contrac(on Algorithm Design and Analysis of Algorithms I Coun(ng Mininum Cuts The Number of Minimum Cuts NOTE: A graph can have multiple min cuts. [e.g., a tree with n vertices has (n-1) minimum cuts] QUESTION: Whats the largest num
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Course: Algorithms: Design And Analysis
QuickSort Design and Analysis of Algorithms I The Par00on Subrou0ne Par00oning Around a Pivot Key Idea : par00on array around a pivot element. -Pick element of array pivot -Rearrange array so that -LeE of pivot
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Course: Algorithms: Design And Analysis
QuickSort Design and Analysis of Algorithms I Choosing a Good Pivot QuickSort: High-Level DescripBon [ Hoare circa 1961 ] QuickSort (array A, length n) -If n=1 return [ currently unimplemented ] -p = ChoosePi
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Course: Algorithms: Design And Analysis
QuickSort Design and Analysis of Algorithms I Proof of Correctness Induc=on Review Let P(n) = asser=on parameterized by posi=ve integers n. For us : P(n) is Quick Sort correctly sorts every input array of length
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Course: Algorithms: Design And Analysis
Design and Analysis of Algorithms I Lecture Notes on QuickSort Analysis1 1 The Problem We are given an unsorted array A containing n numbers, and need to produce a sorted version (in increasing order) of these same numbers. 2 The Partition Subroutine A ke
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Course: Algorithms: Design And Analysis
QuickSort Overview Design and Analysis of Algorithms I QuickSort Tim Roughgarden The Sor>ng Problem Input : array of n numbers, unsorted Output : Same numbers, sorted in increasing order Assume : all array entries
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Course: Algorithms: Design And Analysis
Probability Review Part I Design and Analysis of Algorithms I Topics Covered Sample spaces Events Random variables ExpectaAon Linearity of ExpectaAon See also: Lehman-Leighton notes (free PDF) Wikibook on D
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Course: Algorithms: Design And Analysis
QuickSort Analysis I: A Decomp- osi8on Principle Design and Analysis of Algorithms I Necessary Background Assump8on: you know and remember (nite) sample spaces, random variables, expecta8on, linearity of expecta8on. Fo
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Course: Decision Analysis Applications: Business Strategy And Public Policy
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Course: On Achievability Via Random Binning
1 On Achievability via Random Binning Ritesh Kolte, Kartik Venkat cfw_rkolte, kvenkat@stanford.edu AbstractIn [1], the authors present a novel tool to establish achievability results in network information theoretic problems. The main idea is to study a s
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Week 8 Discussion 1 "Inspirational and Relational Leadership Perspectives" Please respond to the following: From the first e-Activity, evaluate whether the decision to expose the companys culture, strategy, and secrets to outsiders was or was not a good i
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Course: Introductory Economics A
Li 1 Report on P&G Company Table of Content Li 2 1. Executive Summary This report mainly aims at providing an analysis of P&G Company. The P&G Company is one of the worlds most well-known companies of consumer goods. The components of the report include:
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Brendon Pezzack January 18, 2007 Bio 44x Lab Protocal for Enzyme Lab Identifying unknown regulators by their affects on B-galactosidase activity using an in vivo assay of Escherichia coli and an in vitro assay of purified B-galactosidase from Aspergillus
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Course: Introduction To Philosophy
Cho 1 In support of inference to the best explanation and its application in discrediting skepticism Some philosophers claim that the absence of decisive or conclusive evidence against theories postulating that we are brains in vats (referred to as the B
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Course: Intro To Phil
When we argue for or against some moral claim, we want to make sure our argument is first and foremost valid, or that the concluding moral claim follows logically from our premises. If the argument were invalid, then, even if all our premises were sound,
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Course: Intro To Phil
1) Paloma tries to argue that the mind and the brain are not identical. She tries to prove this through two main premises. The first main premise is A): The mind and the brain have different properties. The second main premise is B): Two things are identi
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Course: Human Rights And Humanitarianism
Examining World Vision A discussion on human rights versus humanitarianism By Ahra Cho Let my heart be broken by the things that break the heart of God. And with that, Bob Pierce, founder of World Vision, began to dedicate his life and career to ameliorat
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Course: Human Rights And Humanitarianism
Organization Essay Proposal Let my heart be broken by the things that break the heart of God. And with that Bob Pierce, founder of World Vision, began to dedicate his life and career to ameliorating the plight of poverty and ensuring the care of orphaned
School: Stanford
Course: Human Rights And Humanitarianism
Cho 1 The Development of Human Rights and Humanitarianism from 18th Century Onwards By Ahra Cho Peter Singer, in his influential piece Famine, Affluence, and Morality, asserts: if it is in our power to prevent something bad from happening, without thereby
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Course: Human Rights And Humanitarianism
Short Response to the Hague Convention of 1907 The increase in interaction among different states coupled with the developing moral sensibilities of the late 19th century and early 20th century led the world to view soldiers and other war participants in
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Course: Performance Of Power
1 Cho Humanitarian Intervention? Although states frequently intervene in the political affairs of another state in which human rights violations are deemed to occur, the legitimacy of such humanitarian interventions remains a point of contention. Note, in
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Course: Modern Political Thought
Response Paper to Mill By Ahra Cho Mill argues that we should encourage experiments in living and various forms of individual eccentricity. How, according to Mill, do these things contribute to social progress? How do they contribute to individual or pers
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Course: Modern Political Thought
Response Paper to Burke By Ahra Cho Question: Burke argues that government is not made in virtue of natural rights. Outline what you take to be the central thrust of Burkes critique of natural rights. Burke has issues with using the concept of natural rig
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Course: Governing The 21st Century
Response Paper to Readings on the ICC Indictment of President Bashir By Ahra Cho In 2003, the Sudanese government headed by President Omar Hassan al-Bashir, violently put down an armed insurgence by domestic rebel groups in Darfur (a western region of Sud
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Course: Governing The 21st Century
Ahra Cho POLISCI238C Response paper for theoretical readings on foreign aid and state failure Selected readings from Dambisa Moyos Dead Aid and Jeffrey Sachs The End of Poverty Despite some recent advancement in macroeconomic policies and democratic devel
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Course: Decision Analysis Applications: Business Strategy And Public Policy
David Marrero MS&E 455 December 2006 Professor Robinson Apple Inc. Decision to rehire Steve Jobs as acting CEO Apple Inc is one of the few companies that upon its inception were on top of the market dominating the majority of market shares throughout thei
School: Stanford
Course: Decision Analysis Applications: Business Strategy And Public Policy
Tim Nguyen MS&E 455 Sony, Blu-ray and the Playstation 3 gambit This paper is an examination of the quality of Sonys decision to incorporate bluray disk technology into its newest console: the Playstation 3. The Decision Makers: The decision maker, Sony Co
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Course: Decision Analysis Applications: Business Strategy And Public Policy
Start-up vQube Decision by an entrepreneur Paper for MS&E 455 Decision Making in Organizations By: Saba Usmani Saba Usmani 05127243 Rajat Verma, MBA from Harvard Business School and an entrepreneur in the Silicon Valley, founded a company, vQube in mid 20
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Course: Decision Analysis Applications: Business Strategy And Public Policy
MS&E 455 Decision Making in Organization Autumn 2006 Final Paper Rise or Fall of G-Tube Pi-Chun Chu Management Science and Engineering Stanford University MS&E 455: Decision Making in Organization Table of Contents Rise or Fall of G-Tube . 0 Table of Cont
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Course: Decision Analysis Applications: Business Strategy And Public Policy
Paper for MS&E 455: Decision Making in Organizations On the Quality of a Real Decision: Xilinx and their Choice for a New Web Content Management and Delivery Platform Rebekah Black December 6, 2006 Background A Silicon Valley Player Xilinx is a Silicon Va
School: Stanford
Course: Decision Analysis Applications: Business Strategy And Public Policy
The Hudood Ordinance: Decisions to be Made in Parliament Nabeel Hasnain MS&E 455 December 08, 2006 On November 15, 2006, Pakistans National Assembly voted to amend the countrys rape laws. Whereas formerly the issue was dealt with under its religious court
School: Stanford
Course: Decision Analysis Applications: Business Strategy And Public Policy
Paper for MS&E 455: Decision Making in Organizations Google and YouTube The Final Verdict Mannan Amin Google and YouTube The Final Verdict Introduction This is my first quarter in the US and I was amazed to see the reactions to Googles decision to purchas
School: Stanford
Course: Decision Analysis Applications: Business Strategy And Public Policy
Raibon 1 Miklos Raibon SUID 5082395 MS&E 455 The Latest Star in Dallas Like many organizations and businesses, sports teams make many decisions that impact a variety of people. The sports industry is a multi-billion dollar industry and some of the most im
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Course: Decision Analysis Applications: Business Strategy And Public Policy
MS&E 455 Paper Lauren Aquino November 26, 2006 Court Case Challenges Presidential Authority 1.0 Description of Situation and Decision Maker The decision makers in this case are the Chief Justices of the US Supreme Court. On June 29, 2006, the Supreme Cour
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Course: Decision Analysis Applications: Business Strategy And Public Policy
Decision Making in Organizations: Tequila Sunrise! Jorge Falcn MS&E 455 December 6, 2006 Introduction One of the primarily objectives of large corporations is to develop and sustain continuous growth. Either by expanding operations to different geographic
School: Stanford
Course: Decision Analysis Applications: Business Strategy And Public Policy
Jessica Yuan MS&E 455: Decision Making in Organizations Final Paper (for Letter Grade) Making Danity Kane: An Analysis of the Decision Process Featured on MTVs Making the Band 3 On December 8 2005, Sean Diddy Combs stood before a live audience of several
School: Stanford
Course: Decision Analysis Applications: Business Strategy And Public Policy
Final Paper for MS&E 455: Decision Making in Organization Stanford University - Autumn 2006 CFDT: Looking Forward to Tomorrow Hong Wang -1- CFDT: Looking Forward to Tomorrow It is a Friday afternoon of November 24, 2006. Wendy Lin sits in front of her com
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Course: Decision Analysis Applications: Business Strategy And Public Policy
Googles acquisition of YouTube for $1.65 billion A good decision? FINAL PAPER for MS&E 455 Professor: Burke Robinson Submitted by: Greg Warman STANFORD UNIVERSITY December 6th, 2006 INTRODUCTION The Decision On October 9th, 2006 the business community ent
School: Stanford
Course: Decision Analysis Applications: Business Strategy And Public Policy
Jack OJALVO ONER MS&E 455 - Final Paper ARE IRANS NUCLEAR AMBITIONS A STRATEGIC THREAT FOR WORLD PEACE? IF YES, WHAT SHOULD BE DONE? As the global actor possessing the power to enforce a solution to the problem, the United States is the one who faces the
School: Stanford
Course: Decision Analysis Applications: Business Strategy And Public Policy
MS&E 455 Decision Making in Organizations Daniel Curiel December 6, 2006 INTRODUCTION This paper addresses the process and evaluation of how I made a decision. First, I will briefly explain and evaluate how I use to make decisions before learning Decision
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Course: Decision Analysis Applications: Business Strategy And Public Policy
Elvina Mintarno, MS&E 455 Final Paper, December 2006, 5125175 The Sale of HiTrust Inc. This paper will discuss details of strategic decisions regarding the sale of HiTrust Inc., including description of the decisions, decision makers, decision situation,
School: Stanford
Course: Decision Analysis Applications: Business Strategy And Public Policy
1 Cordel Robbin-Coker MS&E 455: Decision Making in Organizations December 6, 2006 Americas Team: The Quality of the Dallas Cowboys 2006 Personnel Decision The Dallas Cowboys, known as Americas Team is one of the most storied franchises in the history of t
School: Stanford
Course: Decision Analysis Applications: Business Strategy And Public Policy
Decision Making in Professional Soccer: Real Madrids Acquisition of Ruud van Nistelrooy Alejandro Garcia 12/6/2006 MSE455 Prof. Burke Robinson 1 Introduction In the United States it is football. Around the world it is ftbol, the beautiful sport, the world
School: Stanford
Course: Decision Analysis Applications: Business Strategy And Public Policy
INNOVATIVE MICROFINANCING: Personalizationofcyberspacetransactions KIVA.ORG December6,2006 CarolineGhosn DepartmentofManagementScience&Engineering MS&E455:DecisionMakinginOrganizations ProfessorBurkeRobinson StanfordUniversity Stanford,CA94305 caro29@stan
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Course: Decision Analysis Applications: Business Strategy And Public Policy
Ana Sheila Victorino MSE 455: Decision Making in Organizations December 4th 2007 Professor Burke Robinson What goes Where? The Occupancy of the New City Community Center An analysis of Deviations from Commitment, Decision Process, Values and Stakeholders
School: Stanford
Course: Decision Analysis Applications: Business Strategy And Public Policy
Tein Heik, SEOW Aut08 MS&E 455 Final Paper Sun Microsystems Acquisition of MySQL Word Count: 4,192 I. OBJECTIVE OF PAPER . 3 II. INTRODUCTION TO SUN MICROSYSTEMS . 3 III. MYSQL AND THE ACQUISTION . 5 IV. ASSESSMENT OF DECISION QUALITY . 6 Frame . 6 Values
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Course: Decision Analysis Applications: Business Strategy And Public Policy
Elena V. Pujals MSE 455 Prf. Burke Robinson December 5th, 2007 Decision Making in Organizations: A Classic Tale of Management vs. Trade Union The Writers Guild of America has achieved something that, until now, has proved beyond the powers of the worlds m
School: Stanford
Course: Decision Analysis Applications: Business Strategy And Public Policy
Michelle Odemwingie MS&E 455 Decision Making in Organizations Final Decision Analysis December 5, 2007 The End of an Era There was no Yankees logo on modestly dressed black table or the midnight blue curtain. It was a simple hotel ballroom located in the
School: Stanford
Course: Decision Analysis Applications: Business Strategy And Public Policy
MSandE455 StanfordUniversity FinalPaper PeterQu pqu@stanford.edu I. Decision:UniversityofCaliforniaBudgetCut 2008isadifficultyearforboththeStateofCaliforniaandtheUniversityofCaliforniasystem. Thehousingslumpandfinancialcrisishavelefta$24.3billiondeficiti
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Course: Decision Analysis Applications: Business Strategy And Public Policy
Donald Ike Stanford University MS&E 455: Decision Making in Organizations Burke Robinson EBay Acquires Skype Introduction This paper assesses the decision quality of EBays acquisition of Skye. The six dimension of decision will be taken into account. The
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Course: Decision Analysis Applications: Business Strategy And Public Policy
Samad Nasserian SUID: 5499233 Email: samadn@stanford.edu Paper for MS&E 455: Decision Making in Organizations: Avoiding Traps, Motivating People & Improving Process Honor Code I have complied with the honor code and all work is my own. 1 Introduction and
School: Stanford
Course: Decision Analysis Applications: Business Strategy And Public Policy
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School: Stanford
Course: Decision Analysis Applications: Business Strategy And Public Policy
The Decision of Mr. Nawaz Sharif on boycott threat off Hesaam Esfandyarpour MS&E455 12/7/2007 Yesterday Pakistans former prime minister Nawaz Sharif dropped threats to boycott the ballot in protest against President Pervez Musharrafs declaration of emerge
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Course: Decision Analysis Applications: Business Strategy And Public Policy
Yonah Berwaldt 12/5/07 Professor Robinson MS&E 455 Decision Process Analysis of a City Government Building a New City Community Center Background: The paper analyzes the decision process that a city government, located in a suburban community in Californi
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Course: Decision Analysis Applications: Business Strategy And Public Policy
The KC-135 Replacement Decision Melinda Ratz December 3, 2008 Word Count: 4390 Introduction: KC-135 and KC-10 are the monikers for the two Air Force aerial refueling tanker types. In 2001, the KC-1352 had an average age of more than 40 years and the KC-10
School: Stanford
Course: Decision Analysis Applications: Business Strategy And Public Policy
TheDecisiontoBailout Homeowners,theAmerican Economy,andItself JamesChu MS&E455 12/5/07 2007 will be remembered by investors as the year when the sub-prime mortgage crisis reached unprecedented levels of severity. Foreclosure rates peaked, home prices plum
School: Stanford
Course: Decision Analysis Applications: Business Strategy And Public Policy
MS&E 455: Decision Making in Organizations Student Paper Dec 5, 2007 Takashi Wakabayashi Decision: Selecting Next Head Coach of Japan National Football Team. Introduction We occasionally observe unforeseen situation in which a leader of an organization re
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Course: Decision Analysis Applications: Business Strategy And Public Policy
ABN AMRO Acquisition by RBS-led Consortium Phyo Si MS&E 455 Prof. Burke Robinson Word Cont: 5,806 ABN AMRO Acquisition by RBS-led Consortium 1) Introduction On the 9th of October 2007, a year-long negotiation for the largest bank acquisition in history wa
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Course: Decision Analysis Applications: Business Strategy And Public Policy
MS&E455FinalPaper BrandonKampschuur StanfordUniversity 12.6.2007 Introduction This paper critically evaluates a major strategic decision recently made by FramaTech, Inc. (FTI), a small, privately held business located in the Los Angeles area. The paper b
School: Stanford
Course: Decision Analysis Applications: Business Strategy And Public Policy
Beng Hoe Yip 05077888 MS&E 455: Paper on Decision Making in Organizations By Beng Hoe Yip 12/7/2005 Word Count: 6560 1 Beng Hoe Yip 05077888 Introduction Through history, defining moments in the advancement of decision making were made by certain people,
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Course: Decision Analysis Applications: Business Strategy And Public Policy
When Amazon decided to be more than a retailer (Cloud computing and the aftermath) Alexandros Trimis MS&E 455 12/05/2008 1 Table of Contents 0. Introduction .3 . 1. Background Information .4 . 1.1 The beginning.4 1.2 The culture .4 . 1.2 Retail business a
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Course: Decision Analysis Applications: Business Strategy And Public Policy
MS&E 455: Decision Making in Organizations Famous Contributors in the history of Decision Making Gabriel Woo Stanford University November 27, 2005 Although the field of Decision Analysis was founded in the 20 th century, many of the foundations and tools
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Course: Decision Analysis Applications: Business Strategy And Public Policy
MS&E 455: Decision Making in Organizations Final Paper By: Date: Words: Shu Yee Tan December 7, 2005 6,340 Blaise Pascal Background Pascal was one of the most notable mathematicians and physicists of 17th century and a renowned writer of mystical Christia
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Course: Decision Analysis Applications: Business Strategy And Public Policy
Britney Winters Alfred Pritchard Sloan (1875-1966) Alfred Pritchard Sloan was the long-term president and chairperson of General Motors who made countless contributions to the modern day concept of corporations. He also played a significant role in the au
School: Stanford
Course: Decision Analysis Applications: Business Strategy And Public Policy
Jessica Straalsund Nov. 21, 2005 MS&E 455 Decision-Making of Consumers How do consumers make decisions? Throughout history, many have tried to define the decisionmaking process of humans with regards to consumption of goods- in other words, how humans val
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Course: Decision Analysis Applications: Business Strategy And Public Policy
Significant Contributions to Decision Making Corinne Putt MS&E 455 December 7, 2005 Many individuals have made significant contributions to our decision making throughout time. These contributions have been have been attained through work in various field
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Course: Decision Analysis Applications: Business Strategy And Public Policy
Nicole Nicholas There have been many people who have has a hand in shaping the world as we know it. Therefore examining 12 people who have made contributions to the social science and management theory should provide interesting insight into the field. Da
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Course: Decision Analysis Applications: Business Strategy And Public Policy
Wei Siang NEW SUID: 5109346 MS&E 455 Many historical figures have left huge impressions in the field of decision making in organizations. This paper seeks to highlight 12 of them. Dale Carnegie Dale Carnegie was born in poverty on a farm in Maryville, Mis
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Course: Analog Integrated Circuit Design
Lecture 6 Design Example 2 Extrinsic Capacitance Boris Murmann Stanford University murmann@stanford.edu Copyright 2004 by Boris Murmann B. Murmann EE 214 Lecture 6 (HO#9) 1 Overview Reading 1.6.7 (Parasitic Elements) 7.1, 7.2.0, 7.2.1 (Mille
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Course: Introduction To Time Series Analysis
Examples of Time Series Data Models for Time Series Data Lecture 01, Stat 207, Spring 2012 Examples of Time Series Professor David Donoho 2012-04-03 Professor David Donoho Lecture 01, Stat 207, Spring 2012 Examples of Time Series Data Models for Time Seri
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Course: Economic Analysis II
Professor Jay Bhattacharya Spring 2001 Preview of Rest of the Course We have covered consumer decision making and firm decision making. One more lecture on decision making under uncertainty-next Tuesday. The rest of the lectures will focus on how consume
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Course: INTRODUCTION TO OPTIMIZATION
MS&E111 Introduction to Optimization Prof. Amin Saberi Lecture 8 May 1-3, 2006 1 Two player Zero-Sum games In this section, we consider games in which each of two opponents selects a strategy and receives a payoff contingent on both his own and his oppone
School: Stanford
Course: Analog Integrated Circuit Design
Lecture 24 kT/C Noise Boris Murmann Stanford University murmann@stanford.edu Copyright 2004 by Boris Murmann B. Murmann EE 214 Lecture 24 (HO#32) 1 Overview Introduction Having established the basic noise mechanisms in MOSFETS, today's lectur
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Course: PROGRAMMING METHODOLOGY
CS 188: Ar)cial Intelligence NLP, Games, and Robo)c Cars * So Far: Founda)onal Methods Instructor: Pieter Abbeel University of California, Berkeley Slides by Dan Klein and Pieter Abbeel Now: Advanced Applica)ons We
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Course: PROGRAMMING METHODOLOGY
CS 188: Ar)cial Intelligence Conclusion Contest Results Instructor: Pieter Abbeel University of California, Berkeley Slides by Dan Klein and Pieter Abbeel Ketrina Yim Personal Robo)cs PR1 PR2 (autonomous) Appren)ce
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Course: PROGRAMMING METHODOLOGY
Computer Vision CS 188: Ar)cial Intelligence Advanced Applica)ons: Computer Vision and Robo)cs * Instructor: Pieter Abbeel University of California, Berkeley Slides by Dan Klein and Pieter Abbeel Object Detec)on Obje
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Course: PROGRAMMING METHODOLOGY
Case-Based Learning CS 188: Ar)cial Intelligence Kernels and Clustering Instructor: Pieter Abbeel University of California, Berkeley Slides by Dan Klein and Pieter Abbeel Non-Separable Data Case-Based Reasoning Classica)on from sim
School: Stanford
Course: PROGRAMMING METHODOLOGY
Error-Driven Classica)on CS 188: Ar)cial Intelligence Perceptrons Instructor: Pieter Abbeel University of California, Berkeley Slides by Dan Klein and Pieter Abbeel Errors, and What to Do What to Do About Errors
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Course: PROGRAMMING METHODOLOGY
Machine Learning CS 188: Ar)cial Intelligence Nave Bayes Up un)l now: how use a model to make op)mal decisions Machine learning: how to acquire a model from data / experience Learning parameters (e.g. probabili)
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Course: PROGRAMMING METHODOLOGY
Decision Networks CS 188: Ar)cial Intelligence Decision Networks and Value of Perfect Informa)on Instructor: Pieter Abbeel University of California, Berkeley Slides by Dan Klein and Pieter Abbeel Decision Networks De
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Course: PROGRAMMING METHODOLOGY
Bayes Net Representa)on CS 188: Ar)cial Intelligence Bayes Nets: Inference A directed, acyclic graph, one node per random variable A condi)onal probability table (CPT) for each node A collec)on of distribu)ons ove
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Course: PROGRAMMING METHODOLOGY
Example: Grid World CS 188: Ar)cial Intelligence Markov Decision Processes II A maze-like problem The agent lives in a grid Walls block the agents path Noisy movement: ac)ons do not always go as planned 80%
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Course: PROGRAMMING METHODOLOGY
Probabilis)c Models CS 188: Ar)cial Intelligence Bayes Nets Models describe how (a por)on of) the world works Models are always simplica)ons May not account for every variable May not account for all interac
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Course: PROGRAMMING METHODOLOGY
CS 188: Ar)cial Intelligence Applica)ons of HMMs Today HMMs Demo bonanza! Most-likely-explana)on queries Applica)ons: I Know Why You Went to the Clinic: Risks and Realiza)on of HTTPS Trac Analysis Speech rec
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Course: PROGRAMMING METHODOLOGY
Probability Recap CS 188: Ar)cial Intelligence Bayes Nets: Independence Condi)onal probability Product rule Chain rule X, Y independent if and only if: X and Y are condi)onally independent given Z if
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Course: PROGRAMMING METHODOLOGY
CS 188: Ar)cial Intelligence Bayes Nets: Sampling Bayes Net Representa)on A directed, acyclic graph, one node per random variable A condi)onal probability table (CPT) for each node A collec)on of distribu)ons over
School: Stanford
Course: PROGRAMMING METHODOLOGY
Reinforcement Learning CS 188: Ar)cial Intelligence Reinforcement Learning II We s)ll assume an MDP: A set of states s S A set of ac)ons (per state) A A model T(s,a,s) A reward func)on R(s,a,s) S)ll lookin
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Course: PROGRAMMING METHODOLOGY
Our Status in CS188 CS 188: Ar<cial Intelligence Probability Were done with Part I Search and Planning! Part II: Probabilis<c Reasoning Diagnosis Speech recogni<on Tracking objects Robot mapping Gene<cs Error c
School: Stanford
Course: PROGRAMMING METHODOLOGY
AI Outside of 188: Angry Birds Compe88on CS 188: Ar8cial Intelligence Markov Models h:p:/www.aibirds.org Instructor: Pieter Abbeel University of California, Berkeley Slides by Dan Klein and Pieter Abbeel The Product Rule The Pro
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Course: PROGRAMMING METHODOLOGY
Probability Recap CS 188: Ar)cial Intelligence Hidden Markov Models Condi)onal probability Product rule Chain rule X, Y independent if and only if: X and Y are condi)onally independent given Z if an
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Course: PROGRAMMING METHODOLOGY
Reinforcement Learning CS 188: Ar)cial Intelligence Reinforcement Learning Instructor: Pieter Abbeel University of California, Berkeley Slides by Dan Klein and Pieter Abbeel Reinforcement Learning Example: Learning to
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Course: PROGRAMMING METHODOLOGY
Course Sta CS 188: Ar)cial Intelligence Introduc)on Professor GSIs Alvin Wong Anna Lee James Ferguson Abhishek Kar Ankush Gupta John Du John Schulman Michael Liang Peter Qian Teodor Moldovan Pieter Abbeel Instructor: Pieter Abbeel University
School: Stanford
Course: PROGRAMMING METHODOLOGY
Non<Determinis)c#Search# CS#188:#Ar)cial#Intelligence# # Markov#Decision#Processes# Instructor: Pieter Abbeel University of California, Berkeley Slides by Dan Klein and Pieter Abbeel Example:#Grid#World# Grid#World#Ac)ons# Determinis)c#Grid#World# ! A#maz
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Course: PROGRAMMING METHODOLOGY
Uncertain Outcomes CS 188: Ar)cial Intelligence Uncertainty and U)li)es Instructor: Pieter Abbeel University of California, Berkeley Slides by Dan Klein and Pieter Abbeel Worst-Case vs. Average Case Expec)max Search Why wouldnt w
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Course: PROGRAMMING METHODOLOGY
Today CS 188: Ar)cial Intelligence Informed Search Informed Search Heuris)cs Greedy Search A* Search Graph Search Instructor: Pieter Abbeel University of California, Berkeley Slides by Dan Klein and Pieter Abbeel Recap: Se
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Course: PROGRAMMING METHODOLOGY
What is Search For? CS 188: Ar)cial Intelligence Constraint Sa)sfac)on Problems Assump)ons about the world: a single agent, determinis)c ac)ons, fully observed state, discrete state space Planning: sequences of ac)on
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Course: PROGRAMMING METHODOLOGY
Today CS 188: Ar)cial Intelligence Constraint Sa)sfac)on Problems II Instructor: Pieter Abbeel University of California, Berkeley Ecient Solu)on of CSPs Slides by Dan Klein and Pieter Abbeel Local Search Reminder: CSPs Backtrac
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Course: PROGRAMMING METHODOLOGY
Game Playing State-of-the-Art CS 188: Ar)cial Intelligence Checkers: 1950: First computer player. 1994: First computer champion: Chinook ended 40-year-reign of human champion Marion Tinsley using complete 8-piece endg
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Course: PROGRAMMING METHODOLOGY
Today CS 188: Ar)cial Intelligence Search Agents that Plan Ahead Search Problems Uninformed Search Methods Depth-First Search Breadth-First Search Uniform-Cost Search Instructor: Pieter Abbeel University of Cal
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School: Stanford
Course: The Nature Of The Universe
Physics 15: The Nature of the Universe Summer 2014 Instructor: Dr. Debbie Bard The Early Universe Final week! Homework set 7 due Thursdayth14 usual. as Solutions to H7 will be posted immediately afte HW s are collected at 1pm, so NO LATE TUR INS ALLOWED!
School: Stanford
Course: The Nature Of The Universe
Physics 15: The Nature of the Universe Summer 2014 Instructor: Dr. Debbie Bard Dark Matter and Dark Energy Final week news I'm away at the start of next week I'm back on Thursday for our final lecture Tuesday lecture on Astrobiology will be given by Eric
School: Stanford
Course: The Nature Of The Universe
Physics 15: The Nature of the Universe Summer 2014 Instructor: Dr. Debbie Bard Redshift and the Expanding Universe Advance Summary: Redshift and the Expanding Universe 80 years ago Edwin Hubble discovered that other galaxies are moving away from us at a s
School: Stanford
Course: The Nature Of The Universe
Physics 15: The Nature of the Universe Summer 2014 Instructor: Dr. Debbie Bard Supernovae, neutron stars, pulsars and black holes. Remember white dwarfs? The cold, dead carbon core of a sun-sized star. They fit ~0.6M into a volume sun 0.01 times the radiu
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Course: The Nature Of The Universe
Physics 15: The Nature of the Universe Summer 2014 Instructor: Dr. Debbie Bard Galaxies, Clusters Active Galactic Nuclei Gamma Ray Bursts Discovery of Galaxies By the early 1900s, people were seeing nebulae of stars a over the place. There was quite a deb
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Course: The Nature Of The Universe
Physics 15: The Nature of the Universe Summer 2014 Instructor: Dr. Debbie Bard The Sun and the Stars Exoplanets Advance Summary: Sun/Exoplanets Stars have enough pressure to ignite thermonuclear fusion. Fusion makes heavier elements out of lighter ones. F
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Course: The Nature Of The Universe
Physics 15: The Nature of the Universe Summer 2014 Instructor: Dr. Debbie Bard Our Solar System We're a week away from the midterm! There will be multiple choice questions (~25), short answer questions requir few sentences (~11), and problems involving ca
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Course: The Nature Of The Universe
Physics 15: The Nature of the Universe Summer 2014 Instructor: Dr. Debbie Bard The key to astronomy: Light of all kinds Advance summary: What is Light? Light is an electromagnetic wave It carries energy It always travels at a constant speed in empty space
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Course: The Nature Of The Universe
Physics 15: The Nature of the Universe Summer 2014 Instructor: Dr. Debbie Bard Gravity, Orbital mechanics, the Moon, Tides Advance summary: The Sun-centered model the Solar system. of Kepler's three laws planetary motion. of Newton's laws of motion gravit
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Course: The Nature Of The Universe
Physics 15: The Nature of the Universe Summer 2014 Instructor: Dr. Debbie Bard The key to Astronomy: Telescopes Advance Summary: telescopes Telescopes gather more of the light from a source than your eye. Telescopes contain both optics (to gather and magn
School: Stanford
Course: The Nature Of The Universe
Physics 15: The Nature of the Universe Summer 2014 Instructor: Dr. Debbie Bard Course aim: To understand the universe through astronomy. We'll cover light, telescopes, the Earth, the planets, the Sun, other stars and solar systems, the Galaxy, other galax
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Course: Astronomy Laboratory And Observational Astronomy
Physics/Astronomy 50, Summer 2014 NGC 3081. Credit NASA/ESA and The Hubble Heritage Team Lecture 13: Galaxies Galaxies Huge collections of stars like our Milky Way Contain a few thousand to tens of billions of stars, as well as varying amounts of gas an
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Course: Astronomy Laboratory And Observational Astronomy
Physics 50 - Observational Astronomy The Instructor Josh Meyers - Postdoctoral Researcher Stanford Department of Physics & Kavli Institute for Particle Astrophysics and Cosmology ! Ph.D. studying Type Ia supernovae (exploding stars) ! Current research is
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Course: Astronomy Laboratory And Observational Astronomy
Physics/Astronomy 50, Summer 2014 Crab nebula pulsar (NASA/CXC/SAO) Lecture 11: Exotic Objects Black Holes and Neutron Stars Why These Objects Are Important These are the end-states of many stars They represent matter in extreme states not found on eart
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Course: Astronomy Laboratory And Observational Astronomy
The Sun has Spots! Dark spots reveal rotation rate of the sun. Appearance of spots follows an ~11 year cycle. In each cycle, first appear at high solar latitudes, then migrate to middle, then disppear. The Suns Magnetic Dynamo The sun rotates faster at
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Course: Astronomy Laboratory And Observational Astronomy
How to Chose Lab 4 For your Fourth Lab you can pick one of 4 choices Measurement of the light curve of a variable star. Measurement of the distance to an asteroid using the method of parallax. Measurement of the light curve of a Supernova. Make an HR
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Course: Astronomy Laboratory And Observational Astronomy
Physics/Astronomy 50, Summer 2014 Moon from Stanford Observatory field with C5 at f/10 Lecture 2: Tour of the Universe From my class notes 2 The Celestial Sphere Zenith = Point on the celestial sphere directly overhead Nadir = Point on the c. s. directly
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Course: Astronomy Laboratory And Observational Astronomy
Physics/Astronomy 50, Summer 2014 Lecture 9: The Birth and Death of Stars Stars: Their Births and Deaths What happens to a star as it ages? How are stars born? Very basic cycle: Born because of gravitational contraction of gas clouds During life they
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Course: Astronomy Laboratory And Observational Astronomy
Physics/Astronomy 50, Summer 2014 Lecture 8: The Stars Stellar Properties Stars are massive balls of gas They are distinguished from planets by having nuclear reactions (fusion) generating heat in the core. They have 2 fundamental physical properties w
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Course: Astronomy Laboratory And Observational Astronomy
Converting Signals to Magnitudes To calibrate the CCD, we observe a star with a known magnitude Because of atmospheric turbulence, the signal from the star will be spread over many pixels ! ! For each pixel there is an associated surface brightness in
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Course: Astronomy Laboratory And Observational Astronomy
Lecture 6 - Spectroscopy and the Sun The solar spectrum, showing continuum emission and line absorption Black body spectrum Hot objects radiate. Radiation from dense objects completely characterized by temperature - blackbody radiation Hotter objects a
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Course: Probabilistic System Analysis
EE 178/278A Probabilistic Systems Analysis Spring 2014 Tse/Hussami Lecture 2 Introduction to Probability (cont.)1 Reading: Chapter 1 from Bertsekas and Tsitsiklis. We rst review the example of rolling two fair dice. Roll two fair dice. Then the same space
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Course: Probabilistic System Analysis
EE 178/278A Probabilistic Systems Analysis Spring 2014 Tse/Hussami Lecture 6 The Coupon Collectors Problem: An Application We consider a simplied description of the BitTorrent peer-to-peer le-sharing protocol. When someone uploads a le to BitTorrent, it b
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Course: The Fourier Transform And Its Applications
EE261 Raj Bhatnagar Summer 2009-2010 EE 261 The Fourier Transform and its Applications Midterm Examination 19 July 2010 (a) This exam consists of 4 questions with 12 total subparts for a total of 50 points. (b) The questions dier in length and diculty. Do
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Course: Basic Physics For Solid State Electronics
1. Semiconductor carrier statistics (40 points) Consider a semiconductor with a face-centered cubic lattice and with cubic symmetry. The valence band has a maximum at with an energy E = 0 and with an effective mass m0 = me. (me is the mass of a free elect
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EE263 Dec. 56 or Dec. 67, 2008. Prof. S. Boyd Final exam This is a 24 hour take-home nal exam. Please turn it in at Bytes Cafe in the Packard building, 24 hours after you pick it up. Please read the following instructions carefully. You may use any books,
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Course: Dynamic Systems
MS&E 201 Dynamic Systems Spring 04-05 Final Exam, Page 1 of 8 June 8, 2005 Professor Edison Tse MS&E 201 DYNAMIC SYSTEMS FINAL EXAM 2004-2005 THREE HOURS 180 minutes, total 100 points. Open Book. Open Notes. Write your name on this page of the exam. You w
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CS 276: Information Retrieval and Web Search Open Book Final Examination This examination consists of 16 pages, 10 questions, and 100 points and counts for 30 percent of your final grade. Please write your answers on the exam paper in the spaces provided.
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Course: Programming Paradigms
CS107 J Zelenski Handout #5 Oct 23, 2009 Midterm practice Midterm Exam: Friday, Oct 30 11am-12:15pm Location TBA The midterm exam is next Friday in-class. Open book/notes You may bring your textbooks, notes, handouts, code printouts, etc. to refer to duri
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Course: DECISION ANALYSIS I
MS&E 252 November 29th, 2001 Handout #25, page 1 of 25 SAMPLE FINAL: EES&OR 252 Final Examination (1997-1998) Please do not begin the exam until you are instructed to do so. Name (printed clearly): _ 1. Count the number of pages in this exam. There should
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1 CS229 Practice Midterm Solutions CS 229, Autumn 2011 Practice Midterm Solutions Notes: 1. The midterm will have about 5-6 long questions, and about 8-10 short questions. Space will be provided on the actual midterm for you to write your answers. 2. The
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CS161 Summer 2013 Handout 15S August 19, 2013 Final Project Solutions The overall distribution of scores on the final project was as follows: 30 25 20 15 10 5 0 0 28 29 33 34 38 39 43 44 48 The overall statistics are Mean: 37.4 / 48 (78%) Median: 38 / 48
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Course: Marketing
Organizational Behavior, 15e (Robbins/Judge) Chapter 18 Organizational Change and Stress Management 1) An example of change in the nature of the workforce is an increase in _. A) college attendance B) mergers and consolidations C) capital investment D) di
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Course: Circuits I
E EI O I A FINAL WINTER0 9 NAME I.D.N UMBER SIGNATURE TIME : 3 H OURS OPENB OOKS,O PENN OTES NO P C o TW IRELESSC OMMUNICATION D EVICE STATE Y OUR A SSUMPTIONS ND R EASONING A NO C REDIT F OR A NSWERS ITHOUT R EASONING W (1) (2) ( 3) (4) n6 n6 n2 n6 130 (
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Course: CS229
CS229 Practice Midterm 1 CS 229, Autumn 2007 Practice Midterm Notes: 1. The midterm will have about 5-6 long questions, and about 8-10 short questions. Space will be provided on the actual midterm for you to write your answers. 2. The midterm is meant to
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Course: Introduction To Communication Systems
EE 279 Professor Cox Solution to Final 1. (12pt) a) ii) b) i) iii) c) i) iv) d) vi) 2. (35pt) t Winter 2005-2006 HO # In phase-acceleration modulation we have: f (t ) = f c + K ! x(" )d" . Therefore to recover the signal we should extract the phase
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Course: Database Systems Principles
CS 245 Midterm Exam Winter 2012 This exam is open book and notes. You can use a calculator and your laptop to access course notes and videos (but not to communicate with other people). You have 70 minutes to complete the exam. Print your name: The Honor C
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Course: Programming Paradigms
CS107 Spring 2007 Handou t 43S CS107 Final Exam Solution June 8th, 2007 Proble m 1 : Munchies (10 points) Give n the f ollowing C+ class definition, gene rate code for the munchy:cheeto method. Assu me that the paramete rs ha ve alrea dy been se t up f or
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Course: INVESTMENT SCIENCE
Investment Science Practice Final Exam Problem 1: (Multiple Choice, True/False) A) You are considering a portfolio consisting of positive (>0) amounts of 2 securities with positive correlation between them. The securities have a standard deviation o
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Course: Programming Methodology
CS106A Handout 35 May 20th, 2011 Spring 2011 CS106A Practice Exam Midterm exam: Thursday, May 26th, 7:00-10:00 P.M. Last Names A through L: Cubberley Auditorium Last Names M through Z: Annenberg Auditorium This handout is intended to give you practice sol
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Course: Dynamic Systems
Professor Tse Spring 2007 Dynamic Systems Midterm Solution Problem 1. Short Answer Questions (a) Model Matching Page 1 of 7 The fate of the Universe "Grabber-Holder Model" Reason. The grabber is "expansion push of Big Bang," and the holder is "the gravita
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Course: ECONOMIC GROWTH AND DEVELOPMENT
Economic Growth and Development Professor Olivier de La Grandville Final Answer Keys 1. (40 points) MS&E 249 Autumn 2008 y = a = a(y - ry ) dy a - 1 dr = y a r dy a-1 dr = y a r a-1 ln y = ln r + ln C, where C is the positive constant of integration. a a-
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Course: Organic Monofunctional Compounds
SECOND EXAMINATION Chemistry 35 Wednesday, November 14, 2007 W. H. Huestis Name: _KEY_ Please print Stanford University ID No. TA and section number:_ I have observed the Stanford Honor Code during this examination. Signature:_ INSTRUCTIONS: This examinat
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Course: Programming Paradigms
CS107 J Zelenski Handout #12 Dec 11, 2009 Solutions to final exam We devoted the entirety of Friday to wielding the mighty red pens. Exam stats: median 74, mean 73 and standard deviation of 19. Full histogram below: I tried to make the final a bit less cr
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Course: Programming Methodology
CS106A Midterm Results 80 70 60 50 Statistics Max: Mean: Median: Std. Dev.: (120 pts.) 119 (x 3) 80.14 80.5 21.5 40 30 20 10 0 100-110 111-120 41-50 61-70 31-40 51-60 71-80 81-90 91-100 0-30
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Course: Computer Organization And Systems
CS107 Handout 06 February 29th, 2012 Winter 2012 CS107 Midterm Exam Solution The CS107 midterms have been graded and were [or are being] handed out during this weeks lab sessions. The exam median was a 39.5 out of 50, the average a 37.4, and the standard
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Course: Theory Of Probability
STATISTICS 116 MIDTERM EXAM Thursday May 10, 2007 Name: Student ID: Instructions: 1. Print your name and student ID number. 2. There are six problems, each worth 10 marks each. 3. You must show all your work to get full credit. 4. If you get stuck on a pr
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Course: Computer Organization And Systems
CS107 Handout 05 February 21st, 2012 Winter 2012 CS107 Midterm Exam This is an open-note exam. You can refer to any course handouts, handwritten lecture notes, and printouts of any code relevant to a CS107 assignment. Good luck! SUNet ID: _ Last Name: _ F
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Course: Principles And Models Of Semiconductor Devices
c hv.jz d u e I+"1- e lec<i. cfw_ ra/ - v o l t e-19 f de '77 = *r" tr = erLlpJX J e=-# o( V = - leax ("/ q<o.bJic- fu) q I 'lea uo, l " P " 6 r^x v lN lr"u p-tL Q"wJ- conv,cts (q) tlr Qa @e Fy'h,-r. " .^*oo b/u Sr X AI , ^,.,- ^ r. lr, + h-rn "- " o", t
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Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Midterm Solutions Nov 14th, 2007 Midterm Examination Solutions Grade Distribution Page 1 of 12 Midterm MS&E 252 Decision Analysis I Midterm Solutions Nov 14th, 2007 Grade Distribution by Question Page 2 of 12 Midterm MS&E 252
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Course: Computer Organization And Systems
CS107 Handout 03 Winter 2012 February 15, 2012 CS107 Practice Midterm Exam Midterm Exam: Tuesday, February 21st, 2012 Nvidia Auditorium 7:00 p.m. until 10:00 p.m. Open book/notes You may bring textbooks, notes, handouts, code printouts, etc. to refer to d
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Course: Programming Methodology
CS106A Handout 24 April 28th, 2011 Spring 2011 CS106A Midterm Examination This is an open-note, open-reader exam. You can refer to any course handouts, textbooks, handwritten lecture notes, and printouts of any code relevant to any CS106A assignment. You
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Course: PROB ANALYSIS
MS&E 120 Probabilistic Analysis Autumn 2007 Final Examination Handout #11, Page 1 of 4 Prof. Ross D. Shachter December 12, 2007 MS&E 120: Probabilistic Analysis Final Examination Three Hours. You will lose credit if you do not turn in your work whe
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Course: Programming Methodology
Mehran Sahami CS106A Handout #28 October 24, 2007 Practice Midterm Examination Midterm Time: Tuesday, October 30, 7:008:30P.M. Midterm Location: Kresge Auditorium Portions of this handout by Eric Roberts and Patrick Young This handout is intended to give
School: Stanford
Course: Programming Methodology
Mehran Sahami CS 106A Handout #36 November 4, 2009 CS 106A Graphics Contest Submission deadline: 5pm on Sunday, November 29th Based on a handout by Eric Roberts The graphics programs you write using the acm.graphics package tend to be more exciting than t
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2012 Final Exam Solutions 1. (15 points)Finding Fourier transforms: The following two questions are independent. (a) (5) In communications theory the analytic signal fa (t) of a signal f (t) is dened,
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Course: Web Applications
HTML Webusesadocumentcentricapproach(notpixelcentric):"Displaythefollowing document";Nogeneralpurposepixellevelaccess;Toenableapplications,theWeb providesafewspecialfeatures(forms)plustheabilitytomodifythestructureofthe documentontheflyusingJavascript
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Course: INVESTMENT SCIENCE
Midterm Exam MS&E 242 Autumn 2005-2006, Thursday Oct. 27, Prof. Primbs Open book, Open notes. Calculators Allowed, No Computers 75 Minutes Problem 1 2 3 4 Total The Stanford University Honor Code A. The Honor Code is an undertaking of the students, i
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Course: Economic Analysis II
Econ 51: Midterm Exam Solutions 1 Short Questions (28 points, 7 points each) in two states 1 and 2, respectively. Does the utility function u(x, y ) = 6 + 5x2 + 7y 2 + 14y + 10x represent a vNM preferences? Why or why not? If it does represent a vNM prefe
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http:/math.stanford.edu/~moore/01-02/20/Exams/02mt1sols-2.gif http:/math.stanford.edu/~moore/01-02/20/Exams/02mt1sols-2.gif (1 of 2) [2/5/2008 11:08:27 AM] http:/math.stanford.edu/~moore/01-02/20/Exams/02mt1sols-2.gif http:/math.stanford.edu/~moor
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Course: Programming Methodology
CS106A May 7, 2007 Time Limit: 2 hours Practice Midterm Examination Name (please print) _ Section Leader _ 1 2 3 4 5 Total score initials General Instructions Answer each of the questions given below. Write all of your answers directly on the exam
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Course: Programming Paradigms
CS107 J Zelenski Midterm Solution Handout #8 Nov 2, 2009 Exam stats: median 53, mean 51, and standard deviation 12. The full histogram is below. I was pretty happy with the exam results. There were many extraordinarily good scores, several a point or two
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http:/math.stanford.edu/~moore/99-00/20/00mt1sols-3.gif http:/math.stanford.edu/~moore/99-00/20/00mt1sols-3.gif (1 of 2) [2/5/2008 11:34:12 AM] http:/math.stanford.edu/~moore/99-00/20/00mt1sols-3.gif http:/math.stanford.edu/~moore/99-00/20/00mt1so
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Course: Programming Paradigms
CS107 Spring 2007 Handout 42 June 1, 2007 CS107 Final Exam Practice Problems Exam Facts First Offering: Friday, June 8th at 8:30 a.m. in Cubberly Auditorium. Second Offering: Friday, June 8th at 7:00 p.m. in Cubberly Auditorium. Three hours, open notes, o
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Course: INTRODUCTION TO ACCOUNTING
Accounting 201: Stanford GSB Autumn Quarter, 2007 Final Examination Solutions INSTRUCTIONS Please verify that your exam contains a total of 7 pages, including this one. You must enter your work and solutions in this booklet, within the spaces provide
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Course: Managerial Accounting
Chapter 013, Relevant Costs for Decision Making LO5: Utilization of constrained resource LO2: Adding or dropping a segment LO1: Relevant cost concepts Professional Exam Adapted LO6: Sell or process further LO4: Special orders LO3: Make or buy Other topics
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Course: INFORMATION THEORY
EE 376A/Stat 376A Prof. T. Weissman Information Theory Friday, March 17, 2006 Solutions to Practice Final Problems These problems are sampled from a couple of the actual nals in previous years. 1. (20 points) Errors and erasures. Consider a binary symmetr
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1 CS229 Practice Midterm CS 229, Autumn 2011 Practice Midterm Notes: 1. The midterm will have about 5-6 long questions, and about 8-10 short questions. Space will be provided on the actual midterm for you to write your answers. 2. The midterm is meant to
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Course: Introductory Economics A
Final Exam - Economics 1/Elementary Economics December 11, 2003 Instructions You have 180 minutes to answer the following questions. Total points = 140 Please stop writing as soon as time is up! Answer each question on a separate bluebook as ind
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Course: Programming Methodology
CS106A Handout 24S May 4th, 2011 Spring 2011 CS106A Midterm Examination Solution The midterms are graded, and theyre available for pickup at the Gates Building in the lobby near my office. The median grade was a 26 out of 35, and the standard deviation wa
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AP Statistics Test A Inference Part VI Name _ _ 1. Which of the following is true about Students t-models? I. They are unimodal, symmetric, and bell-shaped. II. They have fatter tails than the Normal model. III. As the degrees of freedom increase, the t-m
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Course: INTRODUCTION TO ECONOMETRICS
Chapter 18 Nomadic Empires and Eurasian Integration 1. Karakorum was a. the central Asian capital of the Mongols. b. the founder of the Mongol Empire. c. the term applied to the Mongol policy of religious toleration. d. the last powerful Mongol ruler. e.
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AP Statistics Test A Inference for Proportions Part V Name _ _ 1. We have calculated a 95% confidence interval and would prefer for our next confidence interval to have a smaller margin of error without losing any confidence. In order to do this, we can I
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Course: PROGRAMMING METHODOLOGY
CS 188 Spring 2014 Introduction to Articial Intelligence Midterm 2 You have approximately 2 hours and 50 minutes. The exam is closed book, closed notes except your two-page crib sheet. Mark your answers ON THE EXAM ITSELF. If you are not sure of your a
School: Stanford
Course: PROGRAMMING METHODOLOGY
CS 188 Spring 2014 Introduction to Articial Intelligence Midterm 2 You have approximately 2 hours and 50 minutes. The exam is closed book, closed notes except your two-page crib sheet. Mark your answers ON THE EXAM ITSELF. If you are not sure of your a
School: Stanford
Course: PROGRAMMING METHODOLOGY
CS 188 Spring 2014 Introduction to Articial Intelligence Midterm 1 You have approximately 2 hours and 50 minutes. The exam is closed book, closed notes except your one-page crib sheet. Mark your answers ON THE EXAM ITSELF. If you are not sure of your a
School: Stanford
Course: PROGRAMMING METHODOLOGY
CS 188 Spring 2014 Introduction to Articial Intelligence Midterm 1 You have approximately 2 hours and 50 minutes. The exam is closed book, closed notes except your one-page crib sheet. Mark your answers ON THE EXAM ITSELF. If you are not sure of your a
School: Stanford
Course: PROGRAMMING METHODOLOGY
CS 188 Spring 2014 Introduction to Articial Intelligence Final You have approximately 2 hours and 50 minutes. The exam is closed book, closed notes except your two-page crib sheet. Mark your answers ON THE EXAM ITSELF. If you are not sure of your answe
School: Stanford
Course: PROGRAMMING METHODOLOGY
CS 188 Spring 2013 Introduction to Articial Intelligence Midterm 1 You have approximately 2 hours. The exam is closed book, closed notes except your one-page crib sheet. Please use non-programmable calculators only. Mark your answers ON THE EXAM ITSEL
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x h s w e p x h s s u w j n h x h n h x j x o x x o h h m m o h x x x r x x x j i x o j n x k x f f u i i w g i u o n x x x x i n x h o x x j o f j i w i j x x k m j k k x n m j j h x p h x f n r x n n i m n n q q w f j h k o f j w l n n k f j o j m x i
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Course: Data Analysis
STATS 202 Homework 1 Hao Chen July 3, 2011 In total: 40 points. Problem 2 (26 points, 2 points each) Classify the following attributes as binary, discrete, or continuous. Also classify them as qualitative (nominal or ordinal) or quantitative (interval or
School: Stanford
Course: Modern Applied Statistics: Learning
STATS 315A Winter 2007 Homework 1 Solutions Prob. #1 (Thanks to Wei Zhen) (a) The function mixG takes a centroid matrix mu, a vector N specifying the number of samples in each group and the noise variance v. mixG <- function (mu, N, v)cfw_ mu <- rbind(mu)
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Course: LINEAR AND NON-LINEAR OPTIMIZATION
MS&E 211 Linear & Nonlinear Optimization Fall 2011 Prof Yinyu Ye Homework Assignment 3: Due Tuesday November 1st at 6:00pm Problem 1. Sensitivity Analysis: (22 points) You have rented a metal detector for two and a half hours. You can spend your time with
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Course: ECONOMIC GROWTH AND DEVELOPMENT
Economic Growth and Development Professor Olivier de La Grandville Problem Set 1 To be returned Friday, Oct 3rd , 2008 MS&E 249 Fall 2008 1. In his classic paper, Robert Solow gives the solution of the differential equation for r, corres sponding to the W
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CS161 Summer 2013 Handout 09S July 31, 2013 Problem Set 4 Solutions Problem One: Insertion Sort Revisited (4 Points) Theorem: E[I] = (n2). Proof: For any pair of positions 1 i < j n, let Cij be an indicator random variable that is 1 if the elements at pos
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Course: Theory Of Probability
Stat 116 Homework 3 Due Wednesday, April 23th. Please show work and justify answers. No credit for a nal answer with no explanation, even if the answer is correct. 1. Airlines nd that each passenger who books a ight fails to check-in with probability 1 in
School: Stanford
Course: LINEAR AND NON-LINEAR OPTIMIZATION
MS&E 211 Linear & Nonlinear Optimization Fall 2011 Prof Yinyu Ye Homework Assignment 3: SOLUTIONS Problem 1. Sensitivity Analysis: (22 points) [2 points each] You have rented a metal detector for two and a half hours. You can spend your time with it searc
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Course: Introduction To Time Series Analysis
Stats 207 Homework 3 Solution May 2, 2012 1. (a) Trough simple recursion we have xt = t 1 i=0 Thus t 1 E (x t ) = i=0 i t i . E(i ti ) = 0, and Var(xt ) = t 1 i=0 2 Var(i ti ) = t 1 2 i = i=0 2 (1 2t ) . 1 2 Thus xt is not stationary because its varian
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Course: MODERN APPLIED STATISTICS: LEARNING
HW 3 Solutions March 18, 2013 Grade distribution: Problems 1 - 5: 12 points each, Problem 6: 15 points for writeup, 15 points for computation. Problem 1 a) Let cfw_X, y denote the full original dataset, and let cfw_X(i) , y(i) denote the dataset with the
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CS161 Summer 2013 Handout 09 July 22, 2013 Problem Set 4 This problem set is all about randomness randomized algorithms, randomized data structures, random variables, etc. By the time you're done with this problem set, we hope that you have a much more nu
School: Stanford
Course: Digital MOS Integrated Circuits
EE313 Winter 2009-10 J. Kim & M. Horowitz Handout #8 page 1 of 10 SOLUTIONS TO HOMEWORK #0 Problem # 1 (1.1) Run HSPICE on etude1.sp. (1.2) Use CScope to look at the DC transfer characteristic curves. Notice that inverters with different ratios have diffe
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Course: Information Theory
EE376A: Homeworks #6 Solutions 1. Cascaded BSCs. Consider the two discrete memoryless channels (X , p1 (y |x), Y ) and (Y , p2 (z |y ), Z ). Let p1 (y |x) and p2 (z |y ) be binary symmetric channels with crossover probabilities 1 and 2 respectively. 1 1 0
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Course: Introduction To Statistical Signal Processing
EE 278B Statistical Signal Processing October 20, 2011 Handout #6 Homework #4 Due Thursday, October 27 1. Coloring and whitening. Let 210 = 1 2 1 . 012 a. Find the coloring and whitening matrices of using the eigenvalue method discussed in lecture slides
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Course: Introduction To Statistical Signal Processing
EE 278B Statistical Signal Processing October 29, 2011 Handout #9 Homework #4 Solutions 1. (10 points) Coloring and whitening. a. We denote the eigenvalue and eigenvector matrices of as and U , respectively. After using linear algebra methods (or Matlab,
School: Stanford
Course: Introduction To Time Series Analysis
STATS 207 Assignment 2 Solution April 30, 2012 1(1.10) a) M SA(A) = E [(xt+l Axt )2 ] = E [x2+l ] 2AE [xt+l xt ] + A2 E [x2 ] t t = (0) 2A (l) + A2 (0) 2 Here, A2 M SE (A) = 2 (0) > 0 and thus MSA(A) is minimized when A satises M SE (A) = 2 (0) 2A (h) = 0
School: Stanford
Course: Data Analysis
Homework 5 Solutions 1) Read Chapter 5 (Sections 5.2, 5.3, 5.5 and 5.6). 2) a) library(class) train<-read.csv("sonar_train.csv",header=FALSE) y<-as.factor(train[,61]) x<-train[,1:60] test<-read.csv("sonar_test.csv",header=FALSE) y_test<-as.factor(test[,61
School: Stanford
Course: Mathematical Methods For Computer Vision, Robotics, And Graphics
CS205 Homework #8 Solutions Problem 1 Give a criterion for the well-posedness of the kth order, scalar, homogeneous, constant-coefficient ODE u(k) + ck-1 u(k-1) + + c1 u + c0 u = 0 (Hint: Transform to a first-order system y = Ay and observe A is
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #11 10/26/2007 Homework Assignment #5 Due on Thursday November 1st, 11:59 pm In this assignment you are required to turn in the probabilistic section only. Again the "Food for thought" section is optional but will help
School: Stanford
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2012 Problem Set Nine Due Friday, December 7 1. (20 points) 2D Fourier Transforms Find the 2D Fourier Transforms of: (a) sin 2ax1 sin 2bx2 Solution: Because the function is separable we have F (sin 2a
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278B Statistical Signal Processing Tuesday, December 6, 2011 Handout #19 Homework #7 Solutions 1. (20 points) Autocorrelation functions. a. The mean function is X (t) = E[At + B ] = E[A]t + E[B ] = 0. The autocorrelation function is RX (t1 , t2 ) = E[(
School: Stanford
Course: The Fourier Transform And Its Applications
EE 261 The Fourier Transform and its Applications Fall 2009 Solutions to Problem Set One 1. Some practice with geometric sums and complex exponentials (5 points each) Well make much use of formulas for the sum of a geometric series, especially in combinat
School: Stanford
Course: Data Analysis
Homework 5 - Stats 202 Page 1 of 2 Homework 5 1) Read Chapter 5 (Sections 5.2, 5.3, 5.5 and 5.6). 2) This question deals with In Class Exercise #34. a) Repeat In Class Exercise #34 for the k-nearest neighbor classifier for k=1,2,.,10. (We did k=1 in class
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278 Statistical Signal Processing Homework #8 Due: Wednesday, December 2 November 18, 2009 Handout #18 1. Discrete-time Wiener process. Let cfw_Zn : n 0 be a discrete-time white Gaussian noise process; that is, Z1 , Z2 , Z3 , . . . are i.i.d. N (0, 1).
School: Stanford
CS276 Problem Set #2 5 questions, 10 points each. Assigned: Due: Tuesday, May 10th 2011 Thursday May 19th 2011 Delivery: Assignments must be submitted by 5 p.m. Pacific on the due date. Problem sets should be handed to TAs in class or left in the box outs
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278B Statistical Signal Processing October 13, 2011 Handout #4 Homework #3 Due Thursday, October 20 1. Estimation vs. detection. Signal X and noise Z are independent random variables, where X= +1 with probability 1 with probability 1 2 1 , 2 and Z U[2,
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Course: The Fourier Transform And Its Applications
EE261 Raj Bhatnagar Summer 2010-2011 EE 261 The Fourier Transform and its Applications Problem Set 3 Due Wednesday 13 July 1. (15 points) Convolution and cross-correlation The cross-correlation (sometimes just called correlation) of two real-valued signal
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278 Statistical Signal Processing Homework #7 Solutions November 20, 2009 Handout #19 1. Convergence examples. Consider the following sequences of random variables dened on the probability space (, F , P), where = cfw_0, 1, . . . , m 1, F is the collec
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CME 305: Discrete Mathematics and Algorithms Instructor: Professor Amin Saberi (saberi@stanford.edu) HW#2 Due 02/11/11 1. In this problem we use the well known simplex algorithm to prove the strong duality theorem for linear programs. For matrix A Rmn and
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278B Statistical Signal Processing Thursday, November 17, 2011 Handout #16 Homework #7 Due Thursday, December 1 1. Autocorrelation functions. Find the autocorrelation functions of a. the process X (t) = At + B of problem 2 in homework 6. b. the process
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Course: Analog Integrated Circuit Design
EE214 Winter 04/05 Page 1 of 1 HOMEWORK #2 Solutions (Due: Monday, October 11, 2004, noon PT) 1. Use Spice to simulate gm/ID vs. VOV, (e.g. as shown on slides 3 and 4 of lecture 4). a) Generate a plot of gm/ID for EE214 NMOS devices with L=0.35m and
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Course: Integrated Circuit Fabrication Processes
EE 212 FALL 09-10 HOMEWORK ASSIGNMENT #2 ASSIGNED: THURSDAY OCT. 1 DUE: THURSDAY OCT. 8 SOLUTION SHEET Reading Assignment: Chapters 3 and 4 in the text. #1. Spend 30 min or so scanning the information in the 2007 ITRS Front End Processes (on the class web
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Course: VLSI Signal Conditioning Circuits
EE315A Spring 2009 B. Murmann Page 1 of 3 HOMEWORK #5 (Due: Tuesday, May 12, 2009, 1pm PT) 1. Consider the idealized single-stage OTA feedback circuit shown below. The OTA is described by the "OTA1" behavioral model discussed in class and has the followin
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Stat240: Homework 1 - due at beginning of class on Friday October 21, 2011 LX = Lai and Xing, Statistical Models and Methods for Financial Markets 1. Problem 1.7 in LX. 2. Problem 2.3 in LX. 3. Problem 2.5 in LX. 4. Problem 3.2 in LX. 5. (Optional) Proble
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Course: Analog Integrated Circuit Design
EE214 Winter 04/05 B. Murmann Handout #4 Page 1 of 2 HOMEWORK #1 (Due: Monday, October 4, 2004, noon PT) You will not need (and should not use) Spice for any part of this problem set. Use simple long channel MOS models in all problems and ignore fi
School: Stanford
Introduction to Stochastic Processes Stat217, Winter 2012 Homework 4 - due at 5:00 pm on Friday, February 10, 2012 TK = Taylor and Karlin, Introduction to Stochastic Modeling, 3rd edition. PK = Pinsky and Karlin, Introduction to Stochastic Modeling, 4th e
School: Stanford
Course: INTRO TO ELECTRONICS
Homework #4 Solution October 27, 2011 Point Distribution of Homework #4 (55 points total) Basic rule: 1. All calculation mistakes get 1 point o. 2. Mistakes in setting up necessary equations such as KCL get 1 point o if KCL is applied at the right nodes.
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Course: DECISION ANALYSIS II
MS&E 352 Handout #23 Decision Analysis II Mar 04, 2009 _ Problem Set #3 Solutions Grade Distribution 35% 30% 25% 20% 15% 10% 5% 0% 0-5 6-10 11-15 16-20 21-25 26-30 31-35 36-40 41-45 46-50 51-55 56-60 61-65 66-70 71-75 76-80 81-85 86-90 91-95 96-100 Proble
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Course: Probability
EE 178 Probabilistic Systems Analysis Homework #2 Due Thursday, January 24, 2008 Handout #2 January 17, 2008 1. Catching the train. The probability that Riddley Walker goes for a run in the morning before work is 2/5. If he runs then the probabilit
School: Stanford
Course: Circuits I
EE101A / Winter 2013 Prof. Simon Wong Homework #7 (Due March 6, 2013) You can use equations already derived in lecture notes or textbook. Please write your Name and Lab Section time on the front page. 1. Sedra & Smith, p. 341, Problem 5.79. The figure sho
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Homework #1 EE 282 Autumn 2008 Professor Kozyrakis Homework Set 1 Due: Wednesday, 10/15/2008, 5pm Please work in groups of 3 students Instructions: Submit to the box outside Gates 310 by the due date above. Show your work, state your assumptions, and just
School: Stanford
EE 261 Fourier Transform and Applications February 16, 2011 Handout #13 Homework #5 Due Friday, February 25 1. Exercises on distributions. a. Let g (t) be a Schwartz function. Show that g (t) (t) = g (0) (t) g (0) (t) . b. Let Tf be the distribution induc
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Course: LINEAR AND NON-LINEAR OPTIMIZATION
MS&E 211 Fall 2011 Linear and Nonlinear Optimization Oct 11, 2011 Prof. Yinyu Ye Homework Assignment 1: Sample Solution Problem 1 Let x1j = tons of waste sent to incinerator j from Palo Alto , x2j = tons of waste sent to incinerator j from Stanford, and y
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Course: Ochem
Name/Section: Chem 33 Problem Set I Winter 2012, Stack & Kanan Due Monday, January 16th, 2012 by 5:00 pm 1. Functional group recognition is central to the study of reactivity in organic chemistry. Given the atom connectivity below, draw one reasonable Lew
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Course: DECISION ANALYSIS II
MS&E 352 Handout #17 Decision Analysis II February 20th, 2009 _ Problem Set #2 Due Thursday February 5th _ 02/20/09 1 of 12 Problem Set #2 Solutions MS&E 352 Handout #17 Decision Analysis II February 20th, 2009 _ Part I Advanced Information Gathering Prob
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Course: Digital Systems I
EE108B Spring 2003-2004 Prof. Kozyrakis EE108b - Problem Set #1 Solutions (Total 100 points) This homework assignment helps you to be familiar with MIPS assembly language. A full reference guide for MIPS instructions is available in section A.10 (Appendix
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Course: Statistical Methods In Finance
#Although results are not given in the solution, you should provide necessary values, #plots or tables in your assignment. #Unless the formula you used can be easily read from the code, e.g. mu <- mean(x) #otherwise you have to write out the formula you u
School: Stanford
Course: Dynamic Systems
MS&E201 Dynamic Systems Winter 2008 Professor Edison Tse Problem Set #1 (not to be turned in) 1. (a) Write the following series (the Fibonacci series, where each element is the sum of the previous two) in the form of a linear, two-state invariant dynamic
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #2 9/27/2007 Homework Assignment #1 1. Please review the entire course guide, as it contains important information regarding our expectations on the homework assignments. 2. You are not required to turn in any of this
School: Stanford
Course: VLSI Signal Conditioning Circuits
EE315A Spring 2009 B. Murmann Page 1 of 1 HOMEWORK #1 (Due: Thursday, April 9, 2009, 1pm PT) 1. Cadence warm-up. Work through the "Virtuoso Tutorial" handout available on the course website under "CAD". Submit a printout of the circuit schematic and phase
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Course: Data Analysis
homework 4 - Stats 202 Page 1 of 1 homework 4 1) Read Chapter 4 (all sections) and Chapter 5 (Section 5.7 only). 2) Do Chapter 4 textbook problem #3 (parts a,b,c,d,e) on pages 198-200. 3) Do Chapter 4 textbook problem #5 (parts b,c only) on page 200. 4) D
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Problem Session 8 What concepts do we expect you to master for the Final Exam? Medical DA The Foundations of DA PIBP, PISP Relevance The Five Rules U-Curves The Delta Property Sensitivity Analysis Information Gathering, Value
School: Stanford
Course: Stochastic Control
EE365, Spring 2011-12 Professors S. Boyd, S. Lall, and B. Van Roy EE365 Homework 1 solutions 1.1 Optimal disposition of a stock. You must sell a total amount B > 0 of a stock in two rounds. In each round you can sell any nonnegative amount of the stock; b
School: Stanford
Introduction to Stochastic Processes Stat217, Winter 2012 Homework 1 - due at 5:00pm on Friday January 20, 2012 TK = Taylor and Karlin, Introduction to Stochastic Modeling, 3rd edition (or Pinsky and Karlin, 4th edition) 1. Problem III.1.1 on page 99 of T
School: Stanford
Course: Digital MOS Integrated Circuits
EE313 Winter 09/10 J. Kim & M. Horowitz Handout # Page 1 of 16 HOMEWORK #3 SOLUTIONS 1. HSPICE Simulation for Velocity Saturated Model (25pts) In the lecture, we learned many short channel effects in MOS transistors. In this problem, you need to run HSPIC
School: Stanford
Course: Introduction To Automata And Complexity Theory
CS 154 Intro. to Automata and Complexity Theory Handout 25 Autumn 2006 David Dill November 14, 2006 Problem Set 6 Due: November 28, 2006 Homework: (Total 100 points) Do the following exercises. Problem 1. [20 points] Prove that the following problem, call
School: Stanford
Course: DISCRETE MATHEMATICS AND ALGORITHMS
CME 305: Discrete Mathematics and Algorithms Instructor: Professor Amin Saberi (saberi@stanford.edu) HW#2 Solutions 1. Let T be a spanning tree of a graph G with an edge cost function c. We say that T has the cycle property if for any edge e T , c(e ) c(e
School: Stanford
Course: LINEAR AND NON-LINEAR OPTIMIZATION
MS&E 211 Linear and Nonlinear Optimization Prof. Yinyu Ye Fall 2007 Oct 9, 2007 Homework Assignment 2: Due 3:15pm Thursday, Oct 18 There is a homework collecting box outside of professor Ye's office (Terman 316) for you to submit your homework. No late ho
School: Stanford
Course: The Fourier Transform And Its Applications
EE 261 The Fourier Transform and its Applications Fall 2009 Solutions to Problem Set Two 1. (10 points) A famous sum You cannot go through life knowing about Fourier series and not know the application to evaluating a very famous sum. Let S (t) be the saw
School: Stanford
Course: DATA STRUCTURES
E40 / Spring 2012 LAB. 1B: SERIAL AND PARALLEL CONNECTIONS The design portion of the prelab is to be done with your lab partner. Each group of two students need to turn in only one report. OBJECTIVES To examine the current, voltage and power characteristi
School: Stanford
Brendon Pezzack Tuesday Group A Partner: Cayde Ritchie Bio 44X Lab Report: Molecular Biology Examining the effects of various trpR (gene) missense mutations on TrpR protein functionality in Escherichia coli using trp/lac operon fusion, X-gal assays, ligat
School: Stanford
Brendon Pezzack Lab Partner: Cayde Ritchie Tues Group A Drew Peterson Studying the affect of different wavelengths of light (green light, red light and blue light) on the photosynthesis of Kalanchoe blossfeldiana using a sodium bicarbonate infiltrating sy
School: Stanford
Statement of Inquiry Brendon Pezzack Will the addition of procaine to a medium containing normal Strongylocentrotus purpuratus eggs and a ratio of 104:1 sperm increase the levels of ployspermy expressed in the fertilized eggs? It has been shown in previou
School: Stanford
Brendon Pezzack Lab Partner: Cayde Ritchie Tues Group A Drew Peterson Analysis of the effects of HCL (hydrochloric acid) and temperature change on the germination of Lactuca Sativa (looseleaf lettuce) seeds in a closed environment. Objective: This lab pur
School: Stanford
CaydeRitchie February3,2007 Biology44XSectionTuesdayA Biology 44X: Genetics Lab Report Recombination Mapping of 2 X-linked Genes, vermillion (bright red eyes) and yellow (yellow body), in Drosophila melanogaster using two-point cross Abstract: A2pointcros
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Brendon Pezzack January 31, 2007 Lab Write-up for Enzymes Genetic Assay: 1. Raw Data (OD): Time in Minutes 0 0.243 0.25 0.223 0.274 control control experiment experiment 10 0.157 0.179 0.184 0.184 20 0.287 0.27 0.272 0.401 30 0.525 0.442 0.324 0.356 40 0.
School: Stanford
Brendon Pezzack January 13, 2007 Group A on Tuesday Biology 44x Lab for Genetics Lab Mapping the Distance Between the X-Linked vermillion (bright red eyes) gene and the yellow (yellow body) gene in the Drosophilia melanogaster using Recombination Frequenc
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Brendon Pezzack January 13, 2007 Group A on Tuesday Biology 44x Lab for Genetics Lab Mapping the Distance Between the X-Linked vermillion (bright red eyes) gene and the yellow (yellow body) gene in the Drosophilia melanogaster using Recombination Frequenc
School: Stanford
Brendon Pezzack February 16, 2007 Group A Tuesday Biology 44x Proposal for Developmental Biology Lab Determining how concentrations of procaine affect rates of ployspermy expression during the fertilization period of Strongylocentrotus purpuratus in norma
School: Stanford
Brendon Pezzack February 16, 2007 Group A Tuesday Biology 44x Lab Report for Developmental Biology Determining the effect of different concentrations of procaine on ployspermy occurrence during the fertilization period of Strongylocentrotus purpuratus in
School: Stanford
Brendon Pezzack Tues Group A Partner: Cayde Ritchie Bio 44X Protocol Number Two (Week Two) Using restriction analysis, miniprep isolation and gel electrophoresis to determine the orientation of a mutant trpR gene and the specific amino acid changes leadin
School: Stanford
Ultra Violet Radiation Decreases Fertilization and prevents cleavage of fertilized Strongylocentrutus purpuratus eggs Abstract: UVR caused an significant decrease in both fertilization and cleavage rates. UVR damages the DNA within all the cells exposed t
School: Stanford
Ultra Violet Radiation Decreases Fertilization and prevents cleavage of fertilized Strongylocentrutus purpuratus eggs Abstract: UVR caused an significant decrease in both fertilization and cleavage rates. UVR damages the DNA within all the cells exposed t
School: Stanford
Through biochemical and genetic assays of the fungus Aspergillus oryzae and the bacteria Escherichia coli to identify the unknown regulator in enzymatic Beta-Galactosidase Activity Synthesis: The letter of our unknown is a non competitive inhibitor, meani
School: Stanford
Through biochemical and genetic assays of the fungus Aspergillus oryzae and the bacteria Escherichia coli to identify the unknown regulator in enzymatic Beta-Galactosidase Activity Objective: The goal of the enzymes lab is to determine the effect of an un
School: Stanford
Aaron Smith Partner: Todd Norwood Thursday, Section C The effects of Gibberellic acid on the Photosynthesis in Kalanchoe blossfeldiana Objective: Gibberellic acid (GA) has been demonstrated to have positive effects upon plant growth in other experiments,
School: Stanford
The Gibberellic Acid and Far Red Light effects on Lactuca sativa seed germination Objective GibberellicAcid(GA)hasbeenshowntopromotegermination.Redlighthasalsobeenproventoincrease germinationrates.Therefore,thisexperimentwillcheckthevalidityofbothGAandfa
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Red Light and Gibberellic Acid have positive effects on the germination rates of Lactuca sativa Abstract: Gibberellic Acid (GA) and far red light should effect the germination rates of Lactuca sativa. Gibberellic Acid hormonally stimulated germination and
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Red Light and Gibberellic Acid have positive effects on the germination rates of Lactuca sativa Abstract: Gibberellic Acid (GA) and far red light should effect the germination rates of Lactuca sativa. Gibberellic Acid hormonally stimulated germination and
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Aaron Smith Partner: Todd Norwood Thursday, Section C The promotional effects of Gibberellic Acid and Far Red Light exposure on Lactuca sativa seed germination Objective: Gibberellic acid (GA) concentration and far red light have both experimentally been
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Red Light and Gibberellic Acid have positive effects on the germination rates of Lactuca sativa Abstract: Gibberellic Acid (GA) and far red light should affect the germination rates of Lactuca sativa. Gibberellic Acid hormonally stimulated germination and
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Photosynthesis rates in Kalanchoe blossfeldiana in the presence of Gibberellic acid Objective GibberllicAcid(GA)haspositiveeffectsonplantgrowth,andwewantabetterunderstandingofhow GAeffectsthephotosynthesisrateinKalanchoeblossfeldiana.Therehavebeenexperim
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+8.25/10 PASS How UV Radiation will affect Fertilization and Blastulation wow! Studying blastulation is so unique and really cool!) of Strongylocentrutus purpuratus Lesser MP, Kruse VA, Barry TM. Exposure to ultraviolet radiation causes apoptosis in dev
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How UV Radiation will affect Fertilization and Blastulation of Strongylocentrutus purpuratus Lesser MP, Kruse VA, Barry TM. Exposure to ultraviolet radiation causes apoptosis in developing sea urchin embryos. J Exp Biol. 2003 Nov;206(Pt 22):4097-103. Obje
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Determining Successful trpR Insertion in Escherichia coli through Alkaline Lysis and Agarose Gel Electrophoresis Objective Statement: The objective of week 2 of is to check if the trpR gene was successfully inserted into the pACYC plasmid specifically che
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Determining the recombination frequency of Drosophila melanogaster using the cross technique to create a genetic map for the X-linked target gene scalloped and white Objective To determine the basic genetic map of two target x-chromosome mutations in Dros
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Determining Successful trpR Insertion in Escherichia coli through Alkaline Lysis and Agarose Gel Electrophoresis Objective Statement: The objective of week 2 of is to check if the trpR gene was successfully inserted into the pACYC plasmid specifically che
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Amplification of a Gene Insertion in Escherichia coli Objective Statement: The objective of this lab is to clone and characterize a mutant version of a gene into the tryptophan repressor in the bacterium Escherichia coli. We will ligate the mutant gene in
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PRELAB 3 MORE OP-AMP CIRCUITS! If you cant fix it, make it a feature. Anonymous OBJECTIVES (Why am I doing this prelab?) To gain insight into op-amp application circuits beyond those considered in Lab 2. To understand the basics of analog filters. To u
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PRELAB 6 ADDITIONAL CIRCUIT CONCEPTS If you dont know where youre going, any path will take you there. Unknown OBJECTIVES (Why am I doing this prelab?) To learn about oscillators and how to simulate them in Spice. By Professor Gregory Kovacs Edited and U
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PRELAB 5 OPTOELECTRONIC CIRCUITS Its o.k. if we lose money on the product, well just make it up in volume! Harvard MBA Graduate OBJECTIVES (Why am I doing this prelab?) To learn about interfaces between the optical world and the electronic world. WHERES
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PRELAB 4 INTERFACE CIRCUITS AAAAAAAHHHHH. ZZZZZZ. FTHFPHTHTF. AAAAAHHHH! EE122 Student Who Tests Circuits with Wet Fingertips OBJECTIVES (Why am I doing this prelab?) To investigate some of the ways we interface electronics to the real world. WHERES MY P
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PRELAB 1 PHYSICAL & VIRTUAL INSTRUMENTS FOR ELECTRONICS The Future Begins Tomorrow! Motto of YoyoDyne Engineering in the movie Buckaroo Banzai OBJECTIVES (Why am I doing this prelab?) Review of basic instruments (physical and virtual). Review of electroni
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PRELAB 5 OPTOELECTRONIC CIRCUITS Its o.k. if we lose money on the product, well just make it up in volume! Harvard MBA Graduate OBJECTIVES (Why am I doing this prelab?) To learn about interfaces between the optical world and the electronic world. WHERES
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SimpleDBOverview 9/18/2008 WhatisSimpleDB? Abasicdatabasesystem Whatishas Heapfiles BasicOperators(Scan,Filter,JOIN,Aggregate) BufferPool Transactions SQLFrontend Thingsitdoesn'thave Queryoptimizer Fancyrelationaloperators(UNION,etc) Recovery
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6.830 Lab 2: Operators Goals Operators: Join, aggregate, lter, etc. Add and delete tuples Buffer pool eviction SQL parser Example Query SELECT * FROM table1, table2 WHERE table1.field1 = table2.field2 AND table1.id > 5 Example Query ! left.1 = right.
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Course: Unix Operaating System
login as: o202009 o202009@unix-online.strayer.edu's password: Last login: Wed Oct 30 23:14:10 2013 from 199.107.67.103 * System: oracle10g.strayer.edu Note: This is a Strayer University system intended for University purposes only . Unauthorized access t
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Week5LabHandout StepsoftheWesternblot _ Prepareproteinsamples Loadgel Rungel Transfer _ Checktransferwith_ Detectproteinsofinterest _ Addan0bodies Develop ThewhysofthestepsoftheWestern! Step1:Overviewandprepara0onofproteinsamples h2p:/5nyurl.com/4tdgn
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Bio44X Week 7 Bio44X Week 7 Objectives (Complete this section by filling in the missing information.) Experiment 1 The Question we are seeking to answer is: The Experiment we will perform to address this question is: Experiment 2 The Question we are seek
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Your Name _Partners Name _ TAs Name _Lab Day/Time _ LAB 6: ROTATION, MOMENT OF INERTIA, AND TORQUE The goal of this lab is to measure the frictional torque of a system and to study collisions involving rotations. Please review the material in chapter 8 of
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Your Name _TA Name _ Partners Names _Section day/time _ LAB 5: COLLISIONS, ENERGY, AND PROJECTILES This lab will allow you to review concepts that you have been exposed to in PH21. Specifically, in this lab you will apply the physics of projectiles and th
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Your Name _TA Name _ Partners Names _Section day/time _ Lab 8: Latent Heat of Fusion The goal of this lab is to help you gain a better understanding of: Where the heat is going (or coming from) during a phase transition (latent heat) you will actually me
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Your Name _TA Name _ Partners Names _Section day/time _ IN-LAB ACTIVITIES I. BUOYANT FORCE ON A LUCITE ROD 1. Buoyant forces: When an object is submerged in a liquid, the liquid exerts a net upward force, the buoyant force, on the object. Archmides princi
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Bio44X Week 3 Bio44X Week 3 Objective (Complete this section by filling in the missing information.) The Question we are asking is: precisely how well does your mutant p53 protein function as a transcriptional activator? The Experiment you will do in Week
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Bio44X Week 5 Bio44X Week 5 Objectives (Complete this section by filling in the missing information.) Experiment 1 The Question we are asking is: The Experiment we will perform to address this question is: Specifically, today we will: Experiment 2 The Qu
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Bio44X Week 8 Bio44X Week 8 Objectives (Complete this section by filling in the missing information.) Experiment 1 The Question we are seeking to answer is: The Experiment we will perform to address this question is: Experiment 2 The Question we are seek
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Bio44X Week 6 Bio44X Week 6 Objectives (Complete this section by filling in the missing information.) Experiment 1 The Question we are asking is: The Experiment we will perform to address this question is: Specifically, today we will: Experiment 2 The Que
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Bio44X Week 4 Bio44X Week 4 Objective (Complete this section by filling in the missing information.) The Question we are asking is: The Experiment we will perform to address this question is: EXPERIMENT Quantitative -Galactosidase Assay Materials Yeast ce
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Bio44X Week 2 Bio44X Week 2 Objectives (Please complete the blank spaces in this section by adding 1-3 sentences, as appropriate) Experiment 1: Analysis of p53-responsive ADE2 Spot Assay Results The Question we are asking is: The Experiment we will perfor
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Bio44X Week 1 Bio44X Week 1 Objectives Developing Facility with Basic Laboratory Techniques Since accuracy is essential to obtaining data that is useful and interpretable, you will begin by practicing various techniques (i.e. pipetting, dilution, microsco
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Separation of Three Compounds Experiment # 2 The Experiment NH2 O CO O H Benzoic acid O O Ethyl 4-aminobenzoate 9-Fluorenone Compounds MW(g/mol) Appearance benzoic acid 122.12 colorless crystalline solid ethyl 4-aminobenzoate 165.19 9-fluorenone 180.2 Mel
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Dehydration of 2-Methylcyclohexanol Experiment # 3 The Experiment In this experiment, you will dehydrate an alcohol with phosphoric acid to yield several different alkenes. The acid protonates the alcohol, and H2O leaves forming a carbocation that can und
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Chapter 5 Build a Photovoltaic Controller Photovoltaic cells are a great source of renewable energy. With the sun directly overhead, there is about 1kW of solar energy (energetic photons) per square meter of area. A photovoltaic panel converts this solar
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EE152 Lab 2 Revision 1, 30 Sep 2013 1 Energy Meter In this lab, youll build and program a meter that measures voltage, current, power, and energy at DC and AC. Assigned: October 1, 2013. Signoffs: Week of October 7, 2013. 1 New Code Download the code from
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EE152 Lab 4 Revision 1, 21 Oct 2013 1 Motor Control Part II Signoffs: Week of October 21, 2013. 1 Introduction In this lab you will implement the speed controller that you designed in the previous lab with real hardware and demonstrate that the motor can
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Course: Modern Statistical Learning
Multivariatedensityestimation &MachineLearning WingHungWong StanfordUniversity Example:multiparameterflowcytometrydata TheBayesiannonparametricproblem x1, x2, xn areindependentr.v.onaspace TheirdistributionQisunknownbutassumedtobe drawnfromapriordistrib
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Course: Modern Statistical Learning
Stats 270/370 Homework 6 Due Wednesday, March 19th Problem Let be the state space, and let T = (T0 , T1 , ) be a sequence of nested bifurcating partitions of . That is T0 = cfw_ T1 = cfw_0 , 1 T2 = cfw_00 , 01 , 10 , 11 Tk = cfw_ : cfw_0, 1k where = (
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Course: Modern Statistical Learning
Stats 270/370 Homework 5 Due Wednesday, March 12th Problem Assume that the parameter space is a bounded interval in R1 . Let (; x) be a function satisfying the properties that E (, X) = p (; x)dx = 0 has a unique solution at = . Consider the following est
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Course: Modern Statistical Learning
Stats 270/370 Homework 4 Due Wednesday, Feb. 26th Problem 1 Let X1 , X2 , , Xn be i.i.d. N (, 2 ) with unknown and 2 . Now let A (X) be the usual 1 condence interval for . That is, s A (X) = cfw_ : |X | t/2 (n 1) , n where t/2 (n 1) is the 1 /2 quantile o
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Course: Modern Statistical Learning
Stats 270/370 Homework 1 Due Wednesday Jan. 22nd In Class Problem 1 Suppose X1 , , Xn are iid Poisson(). Show by direct calculation without using any theorem in mathematical statistics, that (a) X = n i=1 Xi /n is an unbiased estimator for . (b) X is opti
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Course: Modern Statistical Learning
Stats 270/370 Homework 2 Due Wednesday Jan. 29th In Class Problem Let X P , where . Suppose both the sample space X and the parameter space are nite, and let R(, ) be the risk for any decision rule . Consider both pure and randomized rules. Show that any
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Course: Modern Statistical Learning
Stats 270/370 Homework 3 Due on Tuesday, Feb. 11th Problem 1 Maximum entropy prior : Suppose = [0, 1] is the parameter space of . Dene H() = () log ()d as the entropy of (). The maximum entropy prior is the () that maximizes H. However, sometimes certain
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Course: Modern Statistical Learning
ESL Chapter 2 Overview of Supervised Learning Trevor Hastie and Rob Tibshirani Overview of Supervised Learning Notation X: inputs, feature vector, predictors, independent variables. Generally X will be a vector of p real values. Qualitative features are
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Course: Modern Statistical Learning
ESL Chapter 5 Basis Expansions and Regularization Trevor Hastie and Rob Tibshirani Basis Expansions and Regularization For a vector X, we consider models of the form M f (X) = m hm (X) m=1 Examples of hm : 2 hm (X) = Xj , Xj X , . . . hm (X) = |X|, log(
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Course: Modern Statistical Learning
pn$q~qnq i inEg yg~ $ nH nH ~ ~ nn$qq$q3~gyinv$uv qnyn~nnHzQqv| ~ ~ ~ ~ ~ q pgvninn~vtg~ ny q qn 0yy vzq~ np~H ntx qnn~$q $zi~ n@g x yvv@
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Course: Modern Statistical Learning
e t k qz n j cfw_ w n w eo8|h405lh D80eel n cfw_ cfw_n lyF t ~o pF'T`0oD0eeoc0olF cfw_ w n w cfw_ w n y t w n wk t w t tn w y w w cfw_ t wkk cfw_ x cfw_ w y v t k q z w j t p0Tl epeeeoot!e0h0o8ppF & ! " # F 80@h ' x v w t cfw_ t k qz m n t cfw_
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Course: Modern Statistical Learning
Generalized Boosted Models: A guide to the gbm package Greg Ridgeway September 21, 2009 Boosting takes on various forms with dierent programs using dierent loss functions, dierent base models, and dierent optimization schemes. The gbm package takes the ap
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Course: Modern Statistical Learning
Boosting with R Programming A Short Introduction to gbm Package Zhou Yu Supervised by Trevor Hastie Jun 12, 2009 Abstract This is a short introductory tutorial for the gbm package for Gradient Boosting Model by Greg Ridgeway. I will use the Spam data as a
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Course: Modern Statistical Learning
Package gbm August 29, 2013 Version 2.1 Date 2013-05-10 Title Generalized Boosted Regression Models Author Greg Ridgeway <gregridgeway@gmail.com> with contributions from others Maintainer Harry Southworth <harry.southworth@gmail.com> Depends R (>= 2.9.0),
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Course: Ancient Athletics
Quiz 2 Guide Competitions Civic festivals o monetary prizes o only for citizens of that city o more musical and civic group competitions PanHellenic festivals o crowns as prizes for first place only o open to al
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Course: Ancient Athletics
Why engage in sports? -play, utility, prestige Group sport levels: -informal organized corporate Integration into society 1) Individual (how to choose/train, what level to compete, costs/benefits) 2) Family
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CS103: Practice Problems about Relations February 4, 2014. Several of the problems below use relational composition. Composition of relations is a generalization of function composition: If R is a relation from A to B and S is a relation from B to C, then
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NETWORKING 1.DefineNetwork? Anetworkisasetofdevicesconnectedbyphysicalmedialinks.Anetworkisrecursivelyisaconnectionoftwoor morenodesbyaphysicallinkortwoormorenetworksconnectedbyoneormorenodes. 2.WhatisaLink? Atthelowestlevel,anetworkcanconsistoftwoormorec
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Black Box Testing The technique of testing without having any knowledge of the interior workings of the application is Black Box testing. The tester is oblivious to the system architecture and does not have access to the source code. Typically, when perfo
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1.ExplaintheconceptofReentrancy? Itisauseful,memorysavingtechniqueformultiprogrammedtimesharingsystems.AReentrantProcedureisonein whichmultipleuserscanshareasinglecopyofaprogramduringthesameperiod.Reentrancyhas2keyaspects: Theprogramcodecannotmodifyitself
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CprogramBasic 1.Whatisalocalblock? AlocalblockisanyportionofaCprogramthatisenclosedbytheleftbrace(cfw_)andtherightbrace().ACfunction containsleftandrightbraces,andthereforeanythingbetweenthetwobracesiscontainedinalocalblock. Anifstatementoraswitchstatemen
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rvaluecanbedefinedasanexpressionthatcanbeassignedtoanlvalue.Thervalueappearsontheright sideofanassignmentstatement. Unlikeanlvalue,anrvaluecanbeaconstantoranexpression,asshownhere: int x, y; x = 1; y = (x + 1); /* 1 is an rvalue; x is an lvalue */ /* (x +
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Thesameholdstrueforthedecrementoperator(-).Whentheoperatorisplacedbeforethevariable,youaresaid tohaveaprefixoperation.Whentheoperatorisplacedafterthevariable,youaresaidtohaveapostfixoperation. Forinstance,considerthefollowingexampleofpostfixincrementation
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printf("The program's previous state has been restored.\n"); exit(0); printf("I am about to call longjmp and\n"); printf("return to the previous program state.\n"); call_longjmp(); void call_longjmp(void) cfw_ longjmp(saved_state, 1); 9.Whatisanlvalue?
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Isanarrayanexpressiontowhichwecanassignavalue?Theanswertothisquestionisno,becauseanarrayis composedofseveralseparatearrayelementsthatcannotbetreatedasawholeforassignmentpurposes.The followingstatementisthereforeillegal: int x[5], y[5]; x = y; Youcould,how
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all_done: printf("Whew! That wasn't so bad, was it?\n"); Thisexamplecouldhavebeenwrittenmuchbetter,avoidingtheuseofagotostatement.Hereisanexampleofan improvedimplementation: void better_function(void) cfw_ int x; printf("Excuse me while I count to 5000.
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Generally,loopsaredependentononeormorevariables.Yourprogramcancheckthosevariablesoutsidetheloop toensurethattheloopexecutedproperly.Forinstance,considerthefollowingexample: #define REQUESTED_BLOCKS 512 int x; char* cp[REQUESTED_BLOCKS]; /* Attempt (in vai
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/* Notice how you can use the comma operator to perform multiple initializations on the same line. */ i = 0, j = 1, k = 2; printf("i = 0, j = 0, k = 0\n", i, j, k); /* Here, the comma operator is used to execute three expressions in one line: assign k to
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logicerror,youcouldinsertadefaultcasewhichwouldflagthatcondition.Considerthefollowingexample: void move_cursor(int direction) cfw_ switch (direction) cfw_ case UP: cursor_up(); break; case DOWN: cursor_down(); break; case LEFT: cursor_left(); break; case
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Noticethatforthismethodtowork,theconditionalexpressionmustbebasedonavariableofnumerictypeinorder tousetheswitchstatement.Also,theconditionalexpressionmustbebasedonasinglevariable.Forinstance,even thoughthefollowingifstatementcontainsmorethantwoconditions
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Testvariablewithintheifstatement:5 Testvariablewithintheindependentlocalblock:0 Testvariableaftertheifstatement:10 Noticethataseachtest_varwasdefined,ittookprecedenceoverthepreviouslydefinedtest_var.Alsonoticethat whentheifstatementlocalblockhadended,thep
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Course: Cell Biology And Animal Physiology
When starving, phosphatase removes the phosphate groups Pho4 binds importin and enter nucleus to initiate gene expression Mitochondria, Chloroplast, and Peroxisome: Mitochondria: site of ATP synthesis through oxidative phosphorylation o Bounded by a dou
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Course: Cell Biology And Animal Physiology
Bio 42: Midterm I Study Guide (Lecture 1-8) Cell organization; Protein Trafficking I: Cytosolic proteins, post-translational import into nucleus Cell is the smallest unit of independent life o Most primitive are bacteria and cyanobacteria prokaryotes o Ha
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Course: Cell Biology And Animal Physiology
Protein undergo further glycosylation in Golgi o Some sugars trimmed off, others added on Proteolytic processing: o Proteins are synthesized as longer, inactive precursor protein and undergo specific proteolytic cleavage events o Polyproteins: multiple
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Course: Cell Biology And Animal Physiology
Mechanism of translocation: mRNA binds to free ribosome in cytosol and translation begins As soon as signal peptide translated it binds to large 325 kd particle, 7S RNA molecule and six polypeptide chains forming the SRP (signal recognition particle) W
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Course: Cell Biology And Animal Physiology
o Has nuclear pores Smaller than 40kD proteins can diffuse through pore Larger proteins require Nuclear Localization Sequence (NLS) to go in o NES: Nuclear export sequence to leave Studies of Nucleoplasmin localization: Nucleoplasmin was purified and ch
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Course: Chemical Principles I
Chem31A Study Tips for the Final In preparing for the final exam: 1) Get Organized! Pull together the course materials lecture notes, section worksheets and write-ups, problems sets, and your old exams, 2) Make a plan of action you have over two weeks bef
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All Quiet on the Western Front Study Guide Study and Discussion Guide Chapter 1 1. Where are the men at rest? Five miles behind the front 2. Why is there such an abundance of rations? Miscalculation did not count on so much of a lose of life on the front.
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Course: Human Nutrition
2 fatty acids, glycerol, phosphate=phospholipid Pancreas: Makes enzymes to digest all energy-yielding nutrients.Releases bicarbonate to neutralize stomach acid that enters small intestine -phospholipid bilayer: makes up the membrane of the absorptive
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Course: INVESTMENT SCIENCE
MS&E 242 Investment Science Professor: Class Location and Time: Class Description: Enrollment: Web page: Course email: Course Assistants: Staff Assistant: Required Course Text:Investment Science Prerequisites: Honor Code: Homework: 5pm418 Terman or i
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Course: Computer Organization And Systems
CS107 Handout 02 January 9th, 2012 Winter 2012 CS107 Course Outline Rough Outline of What To Expect Introduction to Systems, Lower-level C Constructs o C basics, UNIX development tools, gcc, gdb. o C pointers and arrays, C strings, malloc, realloc, and fr
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Course: Advanced Analog Integrated Circuit Design
EE214B Winter 11-12 D. Allstot Handout #1 Page 1 of 4 STANFORD UNIVERSITY Department of Electrical Engineering EE214B: Advanced Analog Integrated Circuit Design http:/ccnet.stanford.edu/ee214B/ TIME: Class: MWF 11:00-11:50 AM, Thornton 102 Review Session:
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Course: 51
Economics 1A The First Part of a Two Part Sequence in Introductory Economics Stanford University Department of Economics Fall Quarter 2009-2010 John B. Taylor 248 Landau Building, 723-9677 JohnBTaylor@Stanford.Edu Office hours: Mon: 2-3:30, Wed: 11-12 Int
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Course: Chemical Principles I
CME100 Vector Calculus for Engineers V. Khayms Fall 2011 Course Information Sheet Instructor: Vadim Khayms (vadim@stanford.edu) Office hours: Tue. 6:00-8:00pm Phone: (408) 203-0822 TAs: Michael Lesnick (mlesnick@stanford.edu) Ajith Morpathi (ajithm@stanfo
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Course: NUMERICAL LINEAR ALGEBRA
Numerical Linear Algebra M. Gerritsen Autumn 2009 General Information CME 302 provides in-depth knowledge of matrix computations. It covers extensively algorithms for eigenvalue and eigenvector computations, the singular value decomposition and iterative
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CHEM 33 WENDER/SCHWARTZ Lecture: Instructors: Office Hours: Teaching Assistants: Outreach Sessions: TA Office Hours: Piazza: Hour Examinations: Final Examination: SPRING 2012 GENERAL INFORMATION M/W/F 11:00 11:50 AM Braun Auditorium M/W/F 1:15 2:05 PM Bra
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Course: Electricity And Magnetism
TENTATIVE TENTATIVE TENTATIVE TENTATIVE Spring 2006-2007 Department of Physics, Stanford University Physics 43 Electricity and Magnetism (Osheroff) I. STAFF Professor in charge: Professor Douglas D. Osheroff Varian Rm. 150, 723-4228 osheroff@stanf
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Course: Astronomy Laboratory And Observational Astronomy
Department of Physics, Stanford University! Physics 50, Observational Astronomy Summer 2014 General Information! ! Note: Read carefully now and keep for your reference throughout the Summer Quarter. This document and the other course handouts will be post
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Course: Light And Heat
Department of Physics, Stanford University Physics 45 Light and Heat Autumn 2013 Page 1 of 8 PHYSICS 45 SPECIFIC INFORMATION Please read: (a) the PH45-specific policies below and (b) the general Physics 40 series policies in the second part of the syllabu
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Course: Introduction To Decision Making
MS&E 52: Introduction to Decision Making SUM, 3 units, letter graded or credit/no credit at the option of student MW 4:15 5:30, room 300-300 (near corner of Outer Quad closest to Clock Tower) Experienced management consultants share lessons and war storie
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Course: MATHEMATICAL FOUNDATIONS OF COMPUTING
CS103 Fall 2013 Handout #01 September 23, 2013 CS103 Syllabus _ This syllabus contains the topics and readings for lecture, as well as the assignment due dates and exam dates. Depending on how fast we're able to move through the material, we may end up co
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Course: MATHEMATICAL FOUNDATIONS OF COMPUTING
CS103 Fall 2013 Handout 03 September 27, 2013 CS103 and the Stanford Honor Code Thanks to the entire Fall 2013 CS103 staff for helping out with this handout! The Stanford Honor Code is the following: * 1. The Honor Code is an undertaking of the students,
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Course: MATHEMATICAL FOUNDATIONS OF COMPUTING
CS103 Fall 2013 Handout #00 September 23, 2013 Course Information _ Course Overview Welcome to CS103! CS103 is a first course in discrete math, computability theory, and complexity theory. In this course, we'll find the limits of what problems can be solv
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Course: INTRODUCTION TO PROBABILITY AND STATISTICS FOR ENGINEERS
ENGINEERING MATHEMATICS COURSES OFFERED TO FRESHMEN & SOPHOMORES ! A sequence of mathematics courses has been developed for students interested in pursuing degrees in engineering. The sequence will cover important areas of engineering mathematics, such as
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Course: INTRODUCTION TO PROBABILITY AND STATISTICS FOR ENGINEERS
CME106 Introduction to Probability and Statistics for Engineers V. Khayms Summer 2014 Course Information Sheet Instructor: Vadim Khayms (vadim@stanford.edu) Office hours: Tue. 6-8pm Phone: (408) 203-0822 Teaching Assistants: Danny Rehn (rehnd@stanford.edu
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Course: INTRODUCTION TO PROBABILITY AND STATISTICS FOR ENGINEERS
CME106 Introduction to Probability and Statistics for Engineers V. Khayms Summer 2014 Course Outline 1. 2. Introduction to Probability Sample spaces, events, outcomes, axioms of probability Counting, permutations, combinations, binomial coefficients Proba
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MS&E 52: Introduction to Decision Making Syllabus SUM, 3 units, letter graded or credit/no credit, at the option of each student MW 4:15 5:30pm, room 200-002 (auditorium in basement of History Corner of Outer Quad) Lessons and war stories from management
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Course: The Public Life Of Science And Technology
STS1:ThePublicLifeofScienceandTechnology Winter2014,5Units MonandWed,11:0012:15,withWeeklySection HewlettTeachingCenter201 Instructors PaulaFindlen(History),pfindle stanfordedu,Wed13pm(200242) n @ . DanMcFarland(Education),mcfarland@stanford.edu,Wed35(CE
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Course: Digital Media In Society
Communication 120/220 Digital Media in Society Spring, 2014 Lectures: M/W 9:00-10:15 Sections TBA Professor Fred Turner TAs: Andreas Katsanevas, David Vannette, Kaiping Zhang Office: 436 McClatchy Hall Office Hours: Wednesday, 4-6 E-mail: fturner@stanford
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Course: Global HIV/AIDS
MED 256 / Hum Bio 156 01 (56223) GLOBAL HIV/AIDS (Seminar) TuTh 4:15 - 5:30PM Li Ka Shing Cnt, rooms 203/2 Mar 31, 2014 - Jun 3, 2014 HumBio 156 / Med 256 Spring Quarter 2014, Stanford Professor: Dr. David Katzenstein Email: davidkk@stanford.edu Office ho
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Course: Introduction To Comparative Politics
Professor Kenneth Schultz Office: Encina West 312 Office hours: Wed. 1-3 or by appt. e-mail: kschultz@stanford.edu Political Science 110D/Y Spring 2014 Classroom: Gates B1 TTh 11am-12:15pm War and Peace in American Foreign Policy Course Description This c
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Course: Introduction To Comparative Politics
POLISCI 4 Section Information TA email oce oce hours Stanford University Spring 2014 Simon Ejdemyr ejdemyr@stanford.edu Encina 433 Mondays 9.30-11.30am Please read this document carefully. Overview Section has three goals. First, it provides an opportunit
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Course: Programming Paradigms I
CS107 Spring 2008 Handout 02 April 2, 2008 CS107 Course Outline Rough Outline of What To Expect: Lower-level C constructs o Data types and representation, binary math galore. o Pointers, references. dynamic memory allocation, the heap and its implementati
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Course: PROGRAMMING METHODOLOGY
CS106A Winter 2012-2013 Handout #01 January 6, 2014 CS106A Syllabus _ This handout contains the tentative syllabus for CS106A. Depending on how quickly we're able to make it through the material, we may end up spending more or less time on each of these t
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Course: DECISION ANALYSIS
MS&E 252 Handout #1 Decision Analysis I September 24, 2013 _ Course Guide to MS&E 252 Foundations of Decision Analysis Welcome to Decision Analysis I. This course guide has been developed to summarize the course logistics. Please read this handout careful
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STANFORD UNIVERSITY Course: MS&E140/240 Accounting for Managers and Entrepreneurs, Summer 2013 Class Days/Hours: Tuesdays and Thursdays from 11:00-12:15p.m. Instructor: Mr. Stanton Office Hours: Tues. 10:00-11:00 a.m. Office: Huang 341 Email: vstanton@sta
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Course: Programming Paradigms
CS107 Spring 2008 Handout 02 April 2, 2008 CS107 Course Outline Rough Outline of What To Expect: Lower-level C constructs o Data types and representation, binary math galore. o Pointers, references. dynamic memory allocation, the heap and its implementati
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Course: Infrastructure Project Development
CEE241A/141A Syllabus Infrastructure Project Development Professor: Gary Griggs TA: Jorge Gonzalez Quarter: Autumn 2013 Units: 3 Grading Criteria: 50% Final Project, 25% Assignments and Quizzes, 25% Class and Project Team Participation Meeting Time: T Th
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Course: Compiler
CS143 Summer 2012 Handout #01 June 25, 2012 CS143 Syllabus _ This handout contains the tentative syllabus for CS143. Depending on how quickly we're able to cover various topics, we may proceed more quickly or more slowly than the syllabus indicates. For t
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Economics 1 Principles of Economics Stanford University Department of Economics Fall Quarter 2013-2014 John B. Taylor 248 Landau Economics Building, 723-9677 JohnBTaylor@Stanford.Edu EconomicsOne.com @EconomicsOne Office hours: Mon: 2-3:30, Wed: 11-12 The
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Course: 51
Econ 50 Syllabus Page 1 of 5 Economics 50 February 2012 [Subject to change] Instructor: Prof. Mark Tendall Office: Landau Economics 147 Office Hours: Tu 3:15-4:45pm; W 9:15-10:45am E-mail: tendall@stanford.edu Stanford University Spring 2011-2012 Please r
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Course: 51
Econ 50 Syllabus Page 1 of 5 Economics 50 February 2012 [Subject to change] Instructor: Prof. Mark Tendall Office: Landau Economics 147 Office Hours: Tu 3:15-4:45pm; W 9:15-10:45am E-mail: tendall@stanford.edu Stanford University Spring 2011-2012 Please r
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Course: 51
Econ 50 Syllabus Page 1 of 5 Economics 50 February 2012 [Subject to change] Instructor: Prof. Mark Tendall Office: Landau Economics 147 Office Hours: Tu 3:15-4:45pm; W 9:15-10:45am E-mail: tendall@stanford.edu Stanford University Spring 2011-2012 Please r
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Course: 51
Economics 1B Syllabus Manuel Amador Ofce: Econ 330 amador@stanford.edu Stanford University Winter Quarter 2008-2009 Syllabus This course is an introduction to macroeconomics: economic behavior at the aggregate level (GDP ination, unemployment, interest ra
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Psychology 10/Statistics 60 Ewart Thomas 1 PSYCHOLOGY 10: STATISTICAL METHODS (M-F 9am, Cemex Aud) General Objectives and Syllabus This course encourages you to acquire an understanding of how numerical methods can be applied productively in social scienc
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Department of Physics, Stanford University PHYSICS 41 - Mechanics, Patricia Burchat Winter 2012-2013 Page 1 Syllabus for PHYSICS 41 Mechanics (This syllabus contains information specific to PHYSICS 41. See the Policies document on Coursework for policies
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Northwestern University Chemistry 101 Summer 2012 Instructors: Dr. Fred Northrup (instructor) Office: Technological Institute GG40 Telephone: (847) 491-7910 Email: f-northrup@northwestern.edu Office Hours: T (2-3 PM), Th (2-3 PM), F (1-2 PM) or by appoint
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Course: Introduction To Computer Science
CS105: Introduction to Computer Science Dr. Steve Cooper Autumn 2010 Class Objectives The course objectives are as follows: To develop a strong background in the fundamentals of computer science, To increase your comfort with computers and with the digita
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Course: America In A Unipolar World
POLISCI 213S. A Post-American World: U.S. Foreign Policy in a Uni-Multipolar System Fall Quarter 2010 Tuesdays, 2:15-4:05 pm Building 200-107 Josef Joffe E 103 Encina Hall 650-724-8010 Office Hours: Tuesdays, 4:15 pm and by appointment jjoffe@stanford.edu
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CS106B Autumn 2012 Handout 02 CS106B Course Syllabus September 24th, 2012 I dont even try to promise a day-by-day lecture schedule, since even the most disciplined and organized of instructors have a difficult time staying on track, and Im far outside the
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Course: Introduction To Developmental Psychology
Psych 60: Introduction to Developmental Psychology T/Th 11:00 12:15 PM, 420-041 Sta Michael Frank (Instructor) Luke Butler (TA) Jason Okonofua (TA) Email address mcfrank@stanford.edu lpbutler@stanford.edu okonofua@stanford.edu Oce 420-278 420-294 420-396
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Course: The Psychology Of Personal Change
1 Wise Interventions (Psych 138/238) Instructor: Greg Walton Room 244, Jordan Hall Phone: 8-4284 Email: gwalton@stanford.edu Website/Forum: On coursework once you have registered Class meets: Monday and Wednesday 2:15-3:30 Building 380, Room 380 Office Ho
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Course: The Psychology Of Personal Change
THEPSYCHOLOGYOFPERSONALCHANGE Astudentinitiatedcourseonthetheoryandpracticeofselfdriven behavioralchange WeekTwo IntroductoryRemarks(15mins) Thechallengeofpersonalchangesmoking,dieting,crime Thiscoursewillbeasupportiveandsafeenvironmentforustounderstandw
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updated 9/19/2012 Chemistry 31A, Autumn 2012 Chemical Principles I: Structure & Energetics Professors Waymouth & Schwartz Course Overview: The science of chemistry evolves through a process of observation, hypothesis, and experimentation. This course is s
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Course: ECONOMIC GROWTH AND DEVELOPMENT
Summer 2009 MS&E249 Economic Growth and Development COURSE SYLLABUS Professor Olivier de La Grandville Office Hours TBA Email lagrandvil@aol.com HU U Teaching Assistant Jihee Kim jiheekim@stanford.edu, Terman 490, Office Hours: TBA HU Main Reference: Oliv
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Course: Applied Mechanics: Statics
E14 Applied Mechanics: Statics Mon/Wed/Fri 12:50-2:05pm, 370-370 Ellen Kuhl, ekuhl@stanford.edu, office hours Wed 2:30-4:00pm, Durand 217 Charbel Eid, ceid@stanford.edu, office hours Tue 6:00-7:30pm, Durand 247 Chris Ploch, cploch@stanford.edu, office hou
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Course: Stochastic Modeling
MS&E 221 Ramesh Johari Syllabus January 10, 2007 Management Science and Engineering 221 Stochastic Modeling Mondays and Wednesdays, 1:15 PM2:30 PM Redwood G-19 3 units Instructor: Ramesh Johari Assistant Professor Management Science and Engineering Terman
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Course: Programming Abstractions (Accelerated)
CS106X Handout 02 January 3rd, 2011 Winter 2011 CS106X Course Syllabus I dont even try to promise a day-by-day lecture schedule, since even the most disciplined and organized of instructors have a difficult time staying on track, and Im far outside the se
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Course: DATA STRUCTURES
Everything Bad is Good For You: The Writing of Science and the Science of Writing | PWR 2 Dr. John Pell johnpell@stanford.edu 325 Sweet Hall | Office Hours Monday 2-3 / Thursday 2-4 Drawing upon the ideas of Steven Johnson's book of the same name, this co
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CS106B Autumn 2011 Handout 02 September 26th, 2011 CS106B Course Syllabus I don't even try to promise a day-by-day lecture schedule, since even the most disciplined and organized of instructors have a difficult time staying on track, and I'm far outside t
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Course: Programming Methodology
Mehran Sahami CS 106A Handout #2 September 26, 2011 CS 106A Syllabus (subject to change at any time by the management) Monday September 26 Welcome to CS106A Administrivia Meet Karel the Robot October 3 Wednesday 28 Programming with Karel Control structure
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Course: Chemical Principles I
updated 9/25/2011 Chemistry 31A, Autumn 2011 Chemical Principles I: Structure & Energetics Professors Dai & Schwartz Course Overview: The science of chemistry evolves through a process of observation, hypothesis, and experimentation. This course is struct
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Course: Discrete Structures
Maggie Johnson CS103B Handout #1 CS103B Syllabus & Information Instructor: Maggie Johnson Email: johnson@cs Office Phone: 723-9798 Office Hours: MW, 9-10 in Gates 185 or by appointment Please send all questions to the TAs. Be sure to check the website for
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Course: Discrete Mathematics For Computer Science
Handout #2 January 9, 2008 CS103A Robert Plummer CS103A Syllabus Please note the frequency of the problem sets in the first half of the quarter. We have learned through experience that frequent practice is the key to learning formal logic and proof skills