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School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #13 10/25/2013 Homework Assignment #4 - Solutions Grade Distribution by Question On the next page you will find a breakdown of how well students did on each question. For each question you will see a bar with three dif
School: Stanford
Course: DECISION ANALYSIS
MS&E252 Decision Analysis I 11/09/2013 Midterm Exam - Solutions MS&E252 Decision Analysis I 11/09/2013 MS&E252 Decision Analysis I 11/09/2013 1) Solution: c Using Deal A and Deal B and applying the substitution rule, we get: 0.6 0.5 $100 0.5 $50 0.5 0.4 0
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Midterm Nov 6th, 2007 Midterm Examination MS&E 252: Decision Analysis I Please read the following instructions carefully! 1. This exam is closed book and closed notes, except for a single sheet (1 side). You may use a calculat
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #7 10/14/2007 Homework Assignment #3 Due on Thursday Oct. 18th 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 you
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
STANFORD UNIVERSITY CS 229, Autumn 2011 Midterm Examination Wednesday, November 9, 6:00pm-9:00pm Question Points 1 Generalized Linear Models /15 2 Gaussian Naive Bayes /15 3 Linear Invariance of Logistic Regression /12 4 2-Regularized SVM /18 5 Uniform Co
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: Randomized Algorithms
CS271 Randomness & Computation Fall 2011 Lecture 2: August 30 Lecturer: Alistair Sinclair Based on scribe notes by: Daniel Chen, Anand Kulkarni; Sudeep Juvekar Thomas Vidick Disclaimer: These notes have not been subjected to the usual scrutiny reserved fo
School: Stanford
Course: Modern Applied Statistics: Learning
ESL Chapter 4 Linear Methods for Classication Trevor Hastie and Rob Tibshirani Linear Methods for Classication Linear regression linear and quadatric discriminant functions example: gene expression arrays reduced rank LDA logistic regression separat
School: Stanford
Course: Modern Applied Statistics: Learning
ESL Chapter 3 Linear Methods for Regression Trevor Hastie and Rob Tibshirani Linear Methods for Regression Outline The simple linear regression model Multiple linear regression Model selection and shrinkagethe state of the art 1 ESL Chapter 3 Linear Me
School: Stanford
Course: BACK FROM AFRICA WORKSHOP
ItseemstodaythatnothingishappeninginAfricaexceptfortheviolence, whichisplaguingeverysinglecountry.Thetypicalwesternnarrativedictatesthatviolenceis endemictoAfricaandthatitisthewestsresponsibilitytopreservewhateversemblanceof peacethatcouldbemustered.Whati
School: Stanford
Course: Writing & Rhetoric 2: The Rhetoric Of Ethnic Identity
Elena Marchetti-Bowick Arturo Heredia Essay 1: Critical Analysis Final Draft 10/20/14 For ethnically and racially diverse individuals, such as Ruben Navarrette and Heidi Durrow, self-induced isolation is a common struggle that often precludes a legitimiza
School: Stanford
Course: MACHINE LEARNING
1 Resampling Detection for Digital Image Forensics John Ho, Derek Ma, and Justin Meyer AbstractA virtually unavoidable consequence of manipulations on digital images are statistical correlations introduced between the pixels. These correlations may not be
School: Stanford
Course: MACHINE LEARNING
Structured Completion Predictors Applied to Image Segmentation Dmitriy Brezhnev, Raphael-Joel Lim, Anirudh Venkatesh December 16, 2011 Abstract Multi-image segmentation makes use of global and local features in an attempt to classify every pixel in an ima
School: Stanford
Course: MACHINE LEARNING
CS229 Project Final Report Sign Language Gesture Recognition with Unsupervised Feature Learning Justin K. Chen, Debabrata Sengupta, Rukmani Ravi Sundaram 1. Introduction The problem we are investigating is sign language recognition through unsupervised fe
School: Stanford
Course: MACHINE LEARNING
CS229/CS229A Final Project Writeup: Supervised Learning - Stock Trend Classifier Submitted: 12/16/2011 ChihChi Kao ckao@stanford.edu 0. Note for teaching staff Unfortunately my project partner, Brain Von Osdol,
School: Stanford
Course: Economic Analysis II
Professor Jay Bhattacharya Spring 2001 Example: Calculating IEPs and Engel Curves Demand II Find the IEP and Engel Curve for a consumer with Recap: last lecture we covered: Income Expansion Paths and Engel curves Inferior and Normal Goods Necessities
School: Stanford
Course: Semiconductor Optoelectronic Devices
1/10/12 EE243 Semiconductor Optoelectronic Devices ! Prof. James Harris! Room 328, Paul Allen Center for Integrated Systems (CISX)! ! Harris@snow.stanford.edu! Web Page - http:/ee.stanford.edu/~harris! (650) 723-9775, (650) 723-4659 fax! Ofce Hours 2: 05
School: Stanford
Course: Infrastructure Project Development
Infrastructure Project Development F13-CEE241A/141A Professor Gary Griggs TA Jorge Gonzalez Class 05 The Planning Phase Study the planning processes used for public sector projects including alternative analyses, project rating and evaluation methods, and
School: Stanford
Introduc)ontoInforma)onRetrieval Introduc)ontoInforma)onRetrieval Informa)onRetrieval Informa)onRetrieval(IR)isndingmaterial(usually documents)ofanunstructurednature(usuallytext) thatsa)sesaninforma)onneedfromwithinlarge collec)ons(usuallystoredoncompute
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: DECISION ANALYSIS
MS&E252 Decision Analysis I 11/09/2013 Midterm Exam - Solutions MS&E252 Decision Analysis I 11/09/2013 MS&E252 Decision Analysis I 11/09/2013 1) Solution: c Using Deal A and Deal B and applying the substitution rule, we get: 0.6 0.5 $100 0.5 $50 0.5 0.4 0
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Midterm Nov 6th, 2007 Midterm Examination MS&E 252: Decision Analysis I Please read the following instructions carefully! 1. This exam is closed book and closed notes, except for a single sheet (1 side). You may use a calculat
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
STANFORD UNIVERSITY CS 229, Autumn 2011 Midterm Examination Wednesday, November 9, 6:00pm-9:00pm Question Points 1 Generalized Linear Models /15 2 Gaussian Naive Bayes /15 3 Linear Invariance of Logistic Regression /12 4 2-Regularized SVM /18 5 Uniform Co
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Solutions to Probabilistic Problems 2001 Practice Final December 6th, 2007 1) Solution: a Alice did not violate any of the rules of actional thought. She stated that the only preference she has is that she goes somewhere from
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Midterm Solutions Nov 14th, 2006 Midterm Examination Solutions Grade Distribution 35 1.00 0.90 30 0.80 25 0.70 0.60 20 0.50 15 0.40 10 0.30 0.20 5 0.10 0 -INF - 0 5 - 10 15 - 20 25 - 30 35 - 40 45 - 50 55 - 60 Cumulative 65 -
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #13 10/25/2013 Homework Assignment #4 - Solutions Grade Distribution by Question On the next page you will find a breakdown of how well students did on each question. For each question you will see a bar with three dif
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #7 10/14/2007 Homework Assignment #3 Due on Thursday Oct. 18th 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 you
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #23 12/7/2007 Homework Assignment #7- Solutions Distinctions These distinctions were prepared by the teaching team and reflect our best belief of the meanings of these terms. A decision diagram shows the structure of a
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout # 8 Due 10/17/2013 Homework Assignment #3 Due on Thursday Oct. 17th 2013, at 11:59 pm Assigned Readings: 1. The Foundation of Decision Analysis (Course Reader I): Finish reading chapter 5 to 7 2. Collection of Readings
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: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #14 10/27/2013 Homework Assignment #5 Due on Thursday October 31th, 11:59 pm (Updated on October 27th, 10:30 am) Assigned Reading The Foundation of Decision Analysis: Chapter 10 and 11. Distinctions From the class lect
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
Course: Elliptic Curves In Cryptography
cfw_ "metadata": cfw_ "name": ", "signature": "sha256:7cc919d0418e750d3e97af0a8e49adb4c7c84acbdeae4d90a024c2163e861c6b" , "nbformat": 3, "nbformat_minor": 0, "worksheets": [ cfw_ "cells": [ cfw_ "cell_type": "markdown", "metadata": cfw_, "sou
School: Stanford
Course: Elliptic Curves In Cryptography
cfw_ "metadata": cfw_ "name": ", "signature": "sha256:ad76069f02458ebf0bcfcb451db5a8f5deaec2cceeff53f6154b180f5ea80b29" , "nbformat": 3, "nbformat_minor": 0, "worksheets": [ cfw_ "cells": [ cfw_ "cell_type": "markdown", "metadata": cfw_, "sou
School: Stanford
Course: Elliptic Curves In Cryptography
cfw_ "metadata": cfw_ "name": ", "signature": "sha256:16fd115403beec522e18660e8ac1fbcdbc15e71f29bc84b9396124980fa811aa" , "nbformat": 3, "nbformat_minor": 0, "worksheets": [ cfw_ "cells": [ cfw_ "cell_type": "markdown", "metadata": cfw_, "sou
School: Stanford
Course: Elliptic Curves In Cryptography
cfw_ "metadata": cfw_ "name": ", "signature": "sha256:5b59d90b7431ffc594cfb1e4d1345a3ad9b88a141a63cb75f45b390d1b3ccc3d" , "nbformat": 3, "nbformat_minor": 0, "worksheets": [ cfw_ "cells": [ cfw_ "cell_type": "markdown", "metadata": cfw_, "sou
School: Stanford
Course: Elliptic Curves In Cryptography
cfw_ "metadata": cfw_ "name": ", "signature": "sha256:a37ac6b4b22957f7c78a1c53d48f5a314ee7bdb3cd474fc36b7343079a81226f" , "nbformat": 3, "nbformat_minor": 0, "worksheets": [ cfw_ "cells": [ cfw_ "cell_type": "markdown", "metadata": cfw_, "sou
School: Stanford
Course: Problem Solving For CS Technical Interview
FundamentalAlgorithmsandDataStructures Worktogetherinagrouptoanswereachofthefollowingquestions.Werecommendusingthese questionsasstudypracticegoingforward. DataStructures 1. Whatisthetimecomplexityofaddinganelementtotheendofadynamicarray?Whatis thetimecomp
School: Stanford
Course: Randomized Algorithms
Discrete Mathematics and Algorithms ICME Refresher Course Austin Benson September 15, 2014 These are the lecture notes for the ICME summer 2014 refresher course on discrete mathematics and algorithms. The material is meant to be preparatory for CME 305, t
School: Stanford
Course: Statistical Methods In Engineering And The Physical Sciences
IEOR 153 Supply Chain Management and Logistics Network Design Spring, 2014 Instructor: Prof. Rob Leachman Office: 4127 Etcheverry Hall Phone: 642-7054 E-mail: leachman@ieor.berkeley.edu Office hours: MW 2-3 or by appointment Course meetings: MWF 10-11 in
School: Stanford
Course: Convex Optimization I
Additional Exercises for Convex Optimization Stephen Boyd Lieven Vandenberghe January 10, 2014 This is a collection of additional exercises, meant to supplement those found in the book Convex Optimization, by Stephen Boyd and Lieven Vandenberghe. These ex
School: Stanford
Course: Fundamentals Of Analog Integrated Circuit Design
EE114/ 214A Review Session 2 Simon Basilico and Yaoyu Tao Stanford University taoyaoyu@stanford.edu basilico@stanford.edu A. Arbabian, R. Dutton, B. Murmann EE 114/214A 1 Important Announcements Start HW2 as soon as possible as it requires HSpice setup a
School: Stanford
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
School: Stanford
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
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: 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
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: 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: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #13 10/25/2013 Homework Assignment #4 - Solutions Grade Distribution by Question On the next page you will find a breakdown of how well students did on each question. For each question you will see a bar with three dif
School: Stanford
Course: DECISION ANALYSIS
MS&E252 Decision Analysis I 11/09/2013 Midterm Exam - Solutions MS&E252 Decision Analysis I 11/09/2013 MS&E252 Decision Analysis I 11/09/2013 1) Solution: c Using Deal A and Deal B and applying the substitution rule, we get: 0.6 0.5 $100 0.5 $50 0.5 0.4 0
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Midterm Nov 6th, 2007 Midterm Examination MS&E 252: Decision Analysis I Please read the following instructions carefully! 1. This exam is closed book and closed notes, except for a single sheet (1 side). You may use a calculat
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #7 10/14/2007 Homework Assignment #3 Due on Thursday Oct. 18th 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 you
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
STANFORD UNIVERSITY CS 229, Autumn 2011 Midterm Examination Wednesday, November 9, 6:00pm-9:00pm Question Points 1 Generalized Linear Models /15 2 Gaussian Naive Bayes /15 3 Linear Invariance of Logistic Regression /12 4 2-Regularized SVM /18 5 Uniform Co
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #23 12/7/2007 Homework Assignment #7- Solutions Distinctions These distinctions were prepared by the teaching team and reflect our best belief of the meanings of these terms. A decision diagram shows the structure of a
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Solutions to Probabilistic Problems 2001 Practice Final December 6th, 2007 1) Solution: a Alice did not violate any of the rules of actional thought. She stated that the only preference she has is that she goes somewhere from
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout # 8 Due 10/17/2013 Homework Assignment #3 Due on Thursday Oct. 17th 2013, at 11:59 pm Assigned Readings: 1. The Foundation of Decision Analysis (Course Reader I): Finish reading chapter 5 to 7 2. Collection of Readings
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Midterm Solutions Nov 14th, 2006 Midterm Examination Solutions Grade Distribution 35 1.00 0.90 30 0.80 25 0.70 0.60 20 0.50 15 0.40 10 0.30 0.20 5 0.10 0 -INF - 0 5 - 10 15 - 20 25 - 30 35 - 40 45 - 50 55 - 60 Cumulative 65 -
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: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #14 10/27/2013 Homework Assignment #5 Due on Thursday October 31th, 11:59 pm (Updated on October 27th, 10:30 am) Assigned Reading The Foundation of Decision Analysis: Chapter 10 and 11. Distinctions From the class lect
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #6 10/4/2013 Homework Assignment #2 Due on Thursday Oct. 10th 11:59 pm Homework Submission Logistics: You can access the MS&E 252 homework submission site from Coursework at http:/coursework.stanford.edu. Click on Subm
School: Stanford
Course: Statistical Methods In Finance
Hints to Assignment 2 October 31, 2012 Problem 2.9 No need to decompose the joint density as log f (Xn , . . . , X1 ) = log f (Xt |Xt1 . . .), instead use the joint density directly in your proof. Just remember all it matters is f being a density and the
School: Stanford
Course: Accounting
Decision Making and Relevant Information 2012 Pearson Prentice Hall. All rights reserved. Decision Models A decision model is a formal method of making a choice, often involving both quantitative and qualitative analyses. Managers often use some variatio
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #17 11/9/2007 Homework Assignment #6 Due on Thursday November 15th, 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
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #23 11/16/2012 Homework Assignment #6 Solutions 60 0 5 10 15 20 25 1 0.9 50 0.8 0.7 40 0.6 0.5 30 0.4 20 0.3 0.2 10 0.1 0 0 Page 1 of 20 HW#6 Solutions MS&E 252 Decision Analysis I Handout #23 11/16/2012 Page 2 of 20 H
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #2 9/26/2013 Homework Assignment #1 You are not required to turn in any of this assignment; however, we expect you to have full knowledge of the material included. Reading 1: Foundations of Decision Analysis (Course Re
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #22 11/30/2007 Homework Assignment #7 Due on Thursday, December 6th 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
Course: DECISION ANALYSIS II
MS&E 352 Handout #2 Decision Analysis II January 6th, 2009 Problem Set 0 Due: January 13, 2009 _ This problem set is a gentle tutorial to the beta distribution, which we shall use extensively in this class. You will need Excel to complete the assignments.
School: Stanford
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
School: Stanford
Course: AI
CS229 Practice Midterm 1 CS 229, Autumn 2010 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
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #12 10/31/2007 Homework Assignment #4 - Solutions Students Distribution: 60 1 0.9 50 0.8 0.7 40 0.6 30 0.5 0.4 20 0.3 0.2 10 0.1 0 9.5-10 0-0.5 0.5-1 1-1.5 1.5-2 2-2.5 2.5-3 3-3.5 3.5-4 4-4.5 4.5-5 5-5.5 5.5-6 6-6.5 6.
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: 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: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #21 11/20/2007 Homework Assignment # Solutions #6 Question Distribution: Page 1 of 20 HW#6 Solutions MS&E 252 Decision Analysis I Handout #21 11/20/2007 Student Distribution: 45 40 35 30 0.6 25 0.5 20 0.4 15 0.3 10 5 0
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: 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: Mathematical Finance
MATH 238 WINTER 2009 PROBLEM SET 1 - SOLUTIONS Problem 1: Let S be the current stock price, K the strike price of the option, T the expiration time of the option, t the current time, ST the stock price at time T , r the risk-free interest rate, c the pric
School: Stanford
Course: MODERN APPLIED STATISTICS: LEARNING
Statistics 315a Homework 1, due Wednesday January 29, 2014. ESL refers to the course textbook, and ESL 2.4 refers to exercise 2.4 in ESL. Since the homework assignments count 70% of your nal grade, you must do them on your own. Problem 1 is computing inte
School: Stanford
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I HO31 Final Examination: Part I December 3rd, 2002 Final Examination 2001-2002: Part I Please read the following instructions carefully! 1. This exam is closed book and closed notes. You may use one double-sided 8'x11' sheet of
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
CS262 Problem Session Problem Set 1 Solutions Special thanks to Cristina Pop Problem 1, Part A (a) Optimal: Global, Ends-Free, Constant Gap penalty (due to existence of introns) Heuristic: BLAST, find regions, post-process to get the full sequence, and
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
EE 284 F. Tobagi Autumn 2010-2011 EE284 Homework Assignment No. 1 Topic: Switching Techniques, Network Topologies Handed out: September 21, 2010 Due: September 30, 2010 in class (Previously September 28 but now extended by 2 days) Total Points: 45 ALL WOR
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Homework 7 Homework Assignment #7 Due on Thursday November 28th 11:59 pm Homework Submission Logistics: You can access the MSE 252 homework submission site from Coursework at http:/coursework.stanford.edu. Click on Submit Home
School: Stanford
Course: DECISION ANALYSIS II
MS&E 352 Handout #1 Decision Analysis II January 6th, 2009 _ Course Guide to MS&E 352 Decision Analysis II Professional Decision Analysis Welcome back, we are glad to see you in "Professional Decision Analysis". DA2 is the second course in the DA sequence
School: Stanford
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
School: Stanford
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
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: 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
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
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: Convex Optimization I
EE364a, Winter 2013-14 Prof. S. Boyd EE364a Homework 8 solutions 8.16 Maximum volume rectangle inside a polyhedron. Formulate the following problem as a convex optimization problem. Find the rectangle R = cfw_x Rn | l x u of maximum volume, enclosed in a
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I 9/26/2013 Homework #1 - Solutions Responsible means we have the ability to choose our response to environmental stimuli. Proactive is the recognition that we are responsible for our own lives. We are where we are today because
School: Stanford
Course: Circuits I
EE101A/Winter 2013 Prof. Simon Wong Homework #2 (Due Wednesday, 1/23/13) 1. Determine the equivalent resistance measured between the two terminals if all resistors are 1K. (This is a 2D hexagon, NOT a 3D cube.) R =? 2. Use Nodal Analysis to determine the
School: Stanford
Course: Introduction To VLSI Systems
EE271 Introduction to VLSI Design Subhasish Mitra Computer Systems Laboratory Stanford University subh@stanford.edu Copyright 2011 by Subhasish Mitra, With significant contributions from Mark Horowitz, Don Stark, and Azita Emami SM EE271 Lecture 1 Notes o
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Course: Introduction To Time Series Analysis
Stat 207 Practice Final Friday June 01, 2012 NAME_ SUID _ Rule: Open Book + a single sheet of notes. There are 4 Pages. Initial Every Page. 1. TRUE/FALSE (write TRUE OR FALSE in full) _ The autocorrelation sequence of an AR(1) model xt = xt-1 + wt is equa
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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: LINEAR AND NON-LINEAR OPTIMIZATION
MS&E 211 Linear and Nonlinear Optimization Prof. Yinyu Ye Fall 2007 Oct 9, 2007 Homework Assignment 2: Sample Solution Problem 1 (a) Let p1 , p2 , p3 be the number of production cycles that Process 1, Process 2 and Process 3 finish, respectively. Then, th
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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
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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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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: 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
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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
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Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #15 11/4/2007 Homework Assignment #5 Solutions Students Distribution: 40 1 0.9 35 0.8 30 0.7 25 0.6 20 0.5 0.4 15 0.3 10 0.2 5 0.1 0 3-3.5 6.5-7 8-8.5 9.5-10 1.5-2 0-0.5 1-1.5 3.5-4 4.5-5 5-5.5 5.5-6 6-6.5 0.5-1 2.5-3
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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: Randomized Algorithms
CS271 Randomness & Computation Fall 2011 Lecture 2: August 30 Lecturer: Alistair Sinclair Based on scribe notes by: Daniel Chen, Anand Kulkarni; Sudeep Juvekar Thomas Vidick Disclaimer: These notes have not been subjected to the usual scrutiny reserved fo
School: Stanford
Course: Modern Applied Statistics: Learning
ESL Chapter 4 Linear Methods for Classication Trevor Hastie and Rob Tibshirani Linear Methods for Classication Linear regression linear and quadatric discriminant functions example: gene expression arrays reduced rank LDA logistic regression separat
School: Stanford
Course: Modern Applied Statistics: Learning
ESL Chapter 3 Linear Methods for Regression Trevor Hastie and Rob Tibshirani Linear Methods for Regression Outline The simple linear regression model Multiple linear regression Model selection and shrinkagethe state of the art 1 ESL Chapter 3 Linear Me
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Course: Modern Applied Statistics: 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: CONVEX OPTIMIZATION I
EE364a Review Disciplined Convex Programming and CVX convex optimization solvers modeling systems disciplined convex programming CVX 1 Convex optimization solvers LP solvers lots available (GLPK, Excel, Matlab's linprog, . . . ) cone solvers typically h
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Course: Global Positioning System
Supplement: Signal Processing Review AA272C, Winter 2014 2014 Frank van Diggelen & Per Enge 1 Three concepts you need: 1. BPSK 2. Mixer f1 f2 cos(wt) 3. Correlator then you can understand the code/frequency search space 2014 Frank van Diggelen & Per En
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Course: Global Positioning System
Supplementary notes on Notation 2013, Frank van Diggelen AA272C GPS, Stanford 1 Nav equation notation Notation from your text book (equation 6.9): pseudorange residuals measured pseudorange expected pseudorange 2013, Frank van Diggelen geometry matrix c
School: Stanford
Course: Global Positioning System
function [dx,sv_pos,G] = lsnav(prs, eph, xo, bu) % % % % % % % % % % % % % % % % % % % % % % % [dx,sv_pos,G] = lsnav(prs, cprs, eph, xo, bu) calculate an unweighted least squares navigation solution, xhat given pseudo-ranges to sats, an initial position,
School: Stanford
Course: Global Positioning System
% lsdemo % script file to run lsnav.m with some real data collected at Wallops Island, VA, U load lsdemo.mat ephMat = ephem; ephem = eph_m2s(ephMat); %change matrix to structure initlla = [-38.02980, 180-75.39094, 0]; %antipodeal point - as far from true
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Lecture 29: Review Reading: All chapters in ISLR STATS 202: Data mining and analysis Sergio Bacallado December 5, 2014 1/9 Announcements Please send us all regrade requests as soon as possible. 2/9 Announcements Please send us all regrade requests as soon
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Lecture 28: Review Reading: All chapters in ISLR. STATS 202: Data mining and analysis Sergio Bacallado December 3, 2014 1 / 15 Announcements Remember to submit Homework 8 by Friday at 10am to get Kaggle credit. 2 / 15 Announcements Remember to submit Home
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Course: Intro To Databases
OLAP and Cubes Activity November 17, 2014 0 Setup and Installation PostgreSQL Installation PostgreSQL and pgAdmin III should already be installed from the Constraints and Triggers activity. If not, please follow the instructions here 1. Loading Data The d
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Course: Convex Optimization
EE364A Final Name: Christopher Bongsoo Choy ID: 05806896 PROBLEM 1 (A) The problem is ( And the log_sum_exp is convex nondecreasing and convex ( ( ) ) is convex so the above function is convex. Let ( ( ( ) ( ( ( ( ( ) ) ( ) ) ( ( ) ( ) ( ) then the proble
School: Stanford
Course: Convex Optimization
EE364A Final Name:ChristopherBongsooChoy ID:05806896 PROBLEM1 (A) Theproblemis Andthelog_sum_expisconvexnondecreasingandisconvexsotheabovefunctionisconvex.Letthentheproblemisconvex Withvariables Sinceisconvex.Expisconvexnondecreasingandlogsumexpisalsoconv
School: Stanford
Course: Topics In Stochastic Analysis
Notes on optimal portfolios Math 238 March 1, 2012 1 The Merton optimal portfolio allocation We look at the optimal asset allocation problem facing an investor who has to decide how much of his or her wealth to invest in a risky asset (X(t) and how much i
School: Stanford
Course: Topics In Stochastic Analysis
The Vasicek model for interest rates Math 238 February 11, 2013 1 One-Factor Vasicek Model We describe briey the simple one-factor Vasicek model for interest rates and review how bonds and bond options are priced under it. The notation is a bit dierent fr
School: Stanford
Course: STATISTICAL METHODS IN FINANCE
Black-Litterman Asset Allocation and Mean-Variance Portfolio Optimization when Means and Covariances of Asset Returns are Unkown Tze Leung Lai Stanford University 2014 1 / 31 Outline Review of Markowitzs portfolio optimization theory and Black-Litterman a
School: Stanford
Course: STATISTICAL METHODS IN FINANCE
Black-Litterman Asset Allocation in a Bayesian Framework Black and Litterman start with a normal assumption for the asset return rt at period t with expected return : rt |, N (, ). (1) To simplify the problem, they implicitly assumed that is known ( is es
School: Stanford
Course: Information Retrieval And Web Search
Introduc)on to Informa)on Retrieval Introduc)on to Informa)on Retrieval Situa*on Thanks to your stellar performance in CS276, you quickly rise to VP of Search at internet retail giant nozama.com. Your
School: Stanford
Course: Information Retrieval And Web Search
5/8/14 Introduc)on to Informa)on Retrieval Classify based on prior weight of class and condiConal parameter for what each word says: Introduction to Information Retrieval CS276: Information Retrieval and Web Search Text Classic
School: Stanford
Course: Information Retrieval And Web Search
4/23/13 Introduc)on*to*Informa)on*Retrieval* ! ! Introduc)on*to*Informa)on*Retrieval* ! ! Who are these people? Introduc*on!to! Informa(on)Retrieval) Probabilis*c!Informa*on!Retrieval! Chris!Manning,!Pandu!Nayak!and! Prabhakar!Raghavan! Introduc)on*to*Inf
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Course: Information Retrieval And Web Search
Introduction to Information Retrieval Introduction to Information Retrieval Lecture 6 I introduced a bug In my anxiety to avoid taking the log of zero, I rewrote Introduction to Information Retrieval 1 log10 tft,d , wt,d 0, CS276 Information Retrieval a
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Course: Information Retrieval And Web Search
Introduction to Information Retrieval Introduction to Information Retrieval Dont forget Introduction to 5 queries for the Stanford intranet (read the piazza post) Information Retrieval CS276: Information Retrieval and Web Search Christopher Manning, Pand
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Course: Information Retrieval And Web Search
5/15/13 Introduc)on to Informa)on Retrieval Introduc)on to Informa)on Retrieval Sec. 15.4 Machine learning for IR ranking? Weve looked at methods for ranking documents in IR Introduc*on to Cosine simi
School: Stanford
Course: Information Retrieval And Web Search
Introduc)on to Informa)on Retrieval Introduc)on to Informa)on Retrieval Todays lecture Web Crawling (Near) duplicate detec*on Introduc*on to Informa(on Retrieval CS276 Informa*on Retrieval and Web Sear
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Course: Information Retrieval And Web Search
Introduc)on to Informa)on Retrieval Introduc)on to Informa)on Retrieval Todays lecture hypertext and links We look beyond the content of documents Introduc*on to We begin to look at the hyperlinks
School: Stanford
Course: Information Retrieval And Web Search
Introduc)on to Informa)on Retrieval Introduc)on to Informa)on Retrieval Todays Topic: Clustering Document clustering Introduc*on to Mo*va*ons Informa(on Retrieval Document representa*ons Success criteri
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Course: Information Retrieval And Web Search
4/22/14 Introduc)on to Informa)on Retrieval Introduc)on to Informa)on Retrieval Summary - BIM Boils down to p (1 ri ) RSV BIM = ciBIM ; ciBIM = log i (1 pi )ri x =q =1 where document relevant (R=1)
School: Stanford
Course: Information Retrieval And Web Search
Introduc)on to Informa)on Retrieval Introduc)on to Informa)on Retrieval Text classica*on Last week: 3 algorithms for text classica*on Introduc*on to Naive Bayes classier Simple, cheap, high bias, line
School: Stanford
Course: Information Retrieval And Web Search
Introduc)on to Informa)on Retrieval Introduc)on to Informa)on Retrieval Plan Last lecture: Introduc*on to Dic*onary data structures Tolerant retrieval Informa(on Retrieval CS276: Informa*on Retrieval a
School: Stanford
Course: Information Retrieval And Web Search
Introduc)on to Informa)on Retrieval Introduc)on to Informa)on Retrieval Todays focus Retrieval get docs matching query from inverted index Scoring+ranking Introduc*on to Informa(on Retrieval Assign
School: Stanford
Course: Information Retrieval And Web Search
/ / Z / / Z Z Documents to be indexed Friends, Romans, countrymen. / Tokenizer / Z Token stream Friends Romans Countrymen Linguistic modules friend Modified tokens roman countryman 2 4 1 2 13 Indexer Inverted index / / Z Sec. 2.1 W / / Z 16 Sec. 2.1
School: Stanford
Course: Computer And Network Security
CS155: Android Malware Jason Franklin Ph.D. Research Associate and Visiting Lecturer Save the Dalai Lama! Start It's March 24th, 2013. A You're a Tibetan activist named Alice B You receive an email from a fellow activist, Bob Image: Kaspersky Labs, https:
School: Stanford
Course: Computer And Network Security
CS155 Spring 2014 Browser Security Model John Mitchell Reported Web Vulnerabilities "In the Wild" Data from aggregator and validator of NVD-reported vulnerabilities Web vs System vulnerabilities XSS peak ! Decline in % web vulns since 2009 n n 49% in 2010
School: Stanford
Course: Computer And Network Security
Where do security bugs come from? Any how do you nd them in the real world? Stanford CS 155 Spring 2014 Alex Stamos CISO, Yahoo Your humble narrator CISO at Yahoo! 2012 - founded Artemis Internet (.secure
School: Stanford
Course: Computer And Network Security
Spring 2014 CS 155 Browser code isolation John Mitchell Modern web sites are complex Modern web site Code from many sources Combined in many ways Sites handle sensitive information ! Financial data Online banking, tax filing, shopping, budgeting, ! Healt
School: Stanford
Course: Computer And Network Security
Web security HTTPS and the Lock Icon Dan Boneh Goals for this lecture Brief overview of HTTPS: How the SSL/TLS protocol works (very briey) How to use HTTPS IntegraFng HTTPS into the browser Lots of user in
School: Stanford
Course: Computer And Network Security
Web Security: Session Management Dan Boneh, Stanford University Dan Boneh Same origin policy: review Review: Same Origin Policy (SOP) for DOM: Origin A can access origin Bs DOM if match on (scheme, domain,
School: Stanford
Course: Computer And Network Security
CS155: Computer Security Isola,on The connement principle Dan Boneh Running untrusted code We often need to run buggy/unstrusted code: programs from untrusted Internet sites: apps, extensions, plug-ins, codecs for media player exposed
School: Stanford
Course: Computer And Network Security
Spring 2014 CS 155 Program Analysis for Security John Mitchell So>ware bugs are serious problems Thanks: Isil and Thomas Dillig App stores App stores How can you tell whether so>ware you Buy Devel
School: Stanford
Course: Introduction To Computer Science | Programming Methodology
Mehran Sahami CS 106A Handout #45 November 22, 2013 Assignment #7FacePamphlet Due: 3:15pm on Friday, December 6th Note: No late days (free or otherwise) may be used on Assignment #7 Your Early Assignment Help (YEAH) hours: 7:00pm-8:00pm, Friday, Nov. 22nd
School: Stanford
Course: Introduction To Computer Science | Programming Methodology
Mehran Sahami CS106A Handout #48 December 4, 2013 Solutions to Practice Final Exam Portions of this handout by Eric Roberts Problem 1: Short answer (15 points) Answer for 1a: When an object is passed into method, a reference to the object (i.e., its addre
School: Stanford
Course: Introduction To Computer Science | Programming Methodology
Mehran Sahami CS 106A Handout #46A December 4, 2013 Solution to Section #9 Parts of this handout by Eric Roberts and Patrick Young 1. Primitive vs. Objects In the first example, the student is thinking a little too literally about the expressions theyve w
School: Stanford
Course: Introduction To Computer Science | Programming Methodology
Mehran Sahami CS106A Handout #47 December 4, 2013 Practice Final Examination Final Exam Time: Thursday, December 12th, 12:15pm to 3:15pm Final Exam Location: Memorial Auditorium Portions of this handout by Eric Roberts This handout is intended to give you
School: Stanford
Course: Introduction To Computer Science | Programming Methodology
Mehran Sahami CS 106A Handout #44 November 20, 2013 Packaging Your Program into a Distributable JAR File Based on a handout by Eric Roberts and Brandon Burr Now that youve written all these wonderful programs, wouldnt it be great if you could package them
School: Stanford
Course: Introduction To Computer Science | Programming Methodology
Mehran Sahami CS 106A Handout #46 December 2, 2013 Section Handout #9: Objects and Data structures Parts of this handout by Eric Roberts and Patrick Young 1. Primitive vs. Objects Let's say a student writes the following line of code in a predicate method
School: Stanford
Course: Introduction To Computer Science | Programming Methodology
Mehran Sahami CS 106A Handout #43 November 18, 2013 Section Handout #8: Data Structures Parts of this handout by Brandon Burr and Patrick Young Your task for this section is to write a program that reads in a file containing flight destinations from vario
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Course: Introduction To Computer Science | Programming Methodology
Mehran Sahami CS 106A Handout #42 November 15, 2013 FlyTunes Program (Data Structures Example) File: Song.java /* * File: Song.java * -* Keeps track of the information for one song * in the music shop, including its name, the band * that it is by, and its
School: Stanford
Course: Introduction To Computer Science | Programming Methodology
Mehran Sahami CS 106A Handout #41 November 13, 2013 MusicShop Program (ComponentListener Example) File: MusicShop.java /* * File: MusicShop.java * -* This program handles the data management for a music * shop, showing which albums are carried and how man
School: Stanford
Course: Introduction To Computer Science | Programming Methodology
Mehran Sahami CS 106A Handout #40 November 13, 2013 Assignment #6NameSurfer Due: 3:15pm on Friday, November 22nd Your Early Assignment Help (YEAH) hours: 7:00pm-8:00pm, Thurs., Nov. 14th in Hewlett 200 The NameSurfer assignment was created by Nick Parlant
School: Stanford
Course: Introduction To Computer Science | Programming Methodology
Mehran Sahami CS 106A Handout #36 November 4, 2013 Assignment #5 Yahtzee! Due: 3:15pm on Wednesday, November 13th Your Early Assignment Help (YEAH) hours: 8:00pm-9:00pm, Tuesday, November 5th in 320-105 Based on a handout written by Eric Roberts and Julie
School: Stanford
Course: Introduction To Computer Science | Programming Methodology
Mehran Sahami CS 106A Handout #27 October 23, 2013 Assignment #4 Hangman Due: 3:15pm on Monday, November 4th Your Early Assignment Help (YEAH) hours: 7:00pm-8:00pm, Thursday, October 24th in Hewlett 200 Based on a handout by Eric Roberts For this assignme
School: Stanford
Course: Introduction To Computer Science | Programming Methodology
Mehran Sahami CS 106A Handout #34 November 4, 2013 Debugging Thanks to Eric Roberts and Nick Parlante for portions of this handout. Much of your time as a computer programmer will likely be spent debugging. This phenomenon is best described by a quotation
School: Stanford
Course: Introduction To Computer Science | Programming Methodology
Mehran Sahami CS 106A Handout #39 November 11, 2013 Section Handout #7: Using Interactors and the Debugger Based on a handout by Eric Roberts 1. Using Interactors The purpose of this problem is to give you some practice using the kind of interactors you n
School: Stanford
Course: Introduction To Computer Science | Programming Methodology
Mehran Sahami CS 106A Handout #35 November 4, 2013 Section Handout #6: More Arrays and HashMaps Portions of this handout by Eric Roberts 1. How Prime! In the third century B.C., the Greek astronomer Eratosthenes developed an algorithm for finding all the
School: Stanford
Course: Introduction To Computer Science | Programming Methodology
Mehran Sahami CS 106A Handout #38 November 11, 2013 Example programs showing interactor usage File: InteractiveDrawFace.java /* * File: InteractiveDrawFace.java * -* This program draws GFaces on the screen, but allows the * use to modify their size and co
School: Stanford
Course: BACK FROM AFRICA WORKSHOP
ItseemstodaythatnothingishappeninginAfricaexceptfortheviolence, whichisplaguingeverysinglecountry.Thetypicalwesternnarrativedictatesthatviolenceis endemictoAfricaandthatitisthewestsresponsibilitytopreservewhateversemblanceof peacethatcouldbemustered.Whati
School: Stanford
Course: Writing & Rhetoric 2: The Rhetoric Of Ethnic Identity
Elena Marchetti-Bowick Arturo Heredia Essay 1: Critical Analysis Final Draft 10/20/14 For ethnically and racially diverse individuals, such as Ruben Navarrette and Heidi Durrow, self-induced isolation is a common struggle that often precludes a legitimiza
School: Stanford
Course: MACHINE LEARNING
1 Resampling Detection for Digital Image Forensics John Ho, Derek Ma, and Justin Meyer AbstractA virtually unavoidable consequence of manipulations on digital images are statistical correlations introduced between the pixels. These correlations may not be
School: Stanford
Course: MACHINE LEARNING
Structured Completion Predictors Applied to Image Segmentation Dmitriy Brezhnev, Raphael-Joel Lim, Anirudh Venkatesh December 16, 2011 Abstract Multi-image segmentation makes use of global and local features in an attempt to classify every pixel in an ima
School: Stanford
Course: MACHINE LEARNING
CS229 Project Final Report Sign Language Gesture Recognition with Unsupervised Feature Learning Justin K. Chen, Debabrata Sengupta, Rukmani Ravi Sundaram 1. Introduction The problem we are investigating is sign language recognition through unsupervised fe
School: Stanford
Course: MACHINE LEARNING
CS229/CS229A Final Project Writeup: Supervised Learning - Stock Trend Classifier Submitted: 12/16/2011 ChihChi Kao ckao@stanford.edu 0. Note for teaching staff Unfortunately my project partner, Brain Von Osdol,
School: Stanford
Course: MACHINE LEARNING
SENTIMENT-BASED MODEL FOR REPUTATION SYSTEMS IN AMAZON Milad Sharif msharif@stanford.edu Soheil Norouzi snorouzi@stanford.edu 1. INTRODUCTION When buyers purchase products from an online retailer such as Amazon, they assess and pay not only f
School: Stanford
Course: MACHINE LEARNING
Sentiment Analysis of Twitter Feeds for the Prediction of Stock Market Movement Ray Chen, Marius Lazer Abstract In this paper, we investigate the relationship between Twitter feed content and stock market movement. Specically, we wish to see if, and how w
School: Stanford
Course: MACHINE LEARNING
1 Sentiment Analysis of Occupy Wall Street Tweets Robert Chang, Sam Pimentel, Alexandr Svistunov Acknowledgements Richard Socher, Andrew Maas, and Maren Pearson. I. Introduction T HE rise of social media has changed political discourse around the world by
School: Stanford
Course: MACHINE LEARNING
Personalized News Prediction and Recommendation Abhishek Arora arorabhi@stanford.edu Dept. of Electrical Engineering Stanford University Abstract: There exist many web based news provider applications (e.g. Pulse News reader application for iPhone/iPad an
School: Stanford
Course: MACHINE LEARNING
Predicting Intraday Price Movements in the Foreign Exchange Market Noam Brown Robert Mundkowsky Sam Shiu Abstract It is commonly assumed that short-term price movements follow a random walk and cannot be predicted. However, in this project we predict next
School: Stanford
Course: MACHINE LEARNING
Scaling for Multimodal 3D Object Detection Andrej Karpathy Stanford karpathy@cs.stanford.edu Abstract We investigate two methods for scalable 3D object detection. We base our approach on a recently proposed template matching algorithm [5] for detecting 3D
School: Stanford
Course: MACHINE LEARNING
CS 229 Final Project Reduced Rank Regression Name : Ka Wai Tsang SID : 005589301 1. Introduction Given m observations of the predictors Xi Rp and the corresponding responses Yi Rn , let Y = [Y1 , Y2 , . . . , Ym ]T and X = [X1 , X2 , . . . , Xm ]T . Suppo
School: Stanford
Course: MACHINE LEARNING
Pulse Project: User-Interest-based News Prediction Yinan Na Jinchao Ye Abstract Pulse is a news recommendation app available on both iPhones and android phones. Predicting news of users interest according to their reading history has always been a hot top
School: Stanford
Course: MACHINE LEARNING
Reddit Recommendation System Daniel Poon, Yu Wu, David (Qifan) Zhang CS229, Stanford University December 11th, 2011 1. Introduction Reddit is one of the most popular online social news websites with millions of registered users. A user can submit content
School: Stanford
Course: MACHINE LEARNING
Promoting Student Success in Online Courses Chuan Yu Foo Yifan Mai Bryan Hooi Frank Chen cyfoo@stanford.edu maiyifan@stanford.edu bhooi@stanford.edu frankchn@stanford.edu 1. Introduction 2.1.3. Automatic Tagging Online education has become popular as an e
School: Stanford
Course: MACHINE LEARNING
Unsupervised Morphological Segmentation with Recursive Neural Network Minh-Thang Luong CS224N/CS229 - Final Project Report 1. Introduction parse tree for a word could be derived from the RNN. Recent works have been successful in applying Recursive Neural
School: Stanford
Course: MACHINE LEARNING
CS229 FINAL PROJECT, AUTUMN 2011 1 Predicting Dow Jones Movement with Twitter Esther Hsu (estherh@stanford.edu) Sam Shiu (bwshiu@stanford.edu) Dan Torczynski (dtor1@stanford.edu) CS229 Final Project, Autumn 2011, Stanford University AbstractThe use of mac
School: Stanford
Course: MACHINE LEARNING
Support Vector Machine Classication of Snow Radar Interface Layers Michael Johnson December 15, 2011 Abstract Operation IceBridge is a NASA funded survey of polar sea and land ice consisting of multiple instruments installed on an airborne platform. The S
School: Stanford
Course: MACHINE LEARNING
Sign Language Classication Using Webcam Images Ruslan Kurdyumov, Phillip Ho, Justin Ng December 16, 2011 Abstract Immediate feedback on sign language gestures can greatly improve sign language education. We seek to classify the English sign language alpha
School: Stanford
Course: MACHINE LEARNING
CS 229 - Project Final report Hooyeon Haden Lee; hlee0 (05382015); 12/16/2011 Title: Using Twitter to Estimate and Predict the Trends and Opinions 1 Introduction was set to zero (hence, predicting the same day trends). In another related work From Tweets
School: Stanford
Course: MACHINE LEARNING
NYC Condo Price Estimation Using NYC Open Data Hari Arul Andres Morales Introduction This project explores the structure of the New York City housing market by predicting the price of condominiums in New York City using the publicly available NYC Open Dat
School: Stanford
Course: MACHINE LEARNING
TACTICAL AND STRATEGIC GAME PLAY IN DOPPELKOPF DANIEL TEMPLETON 1. Abstract The German card game of Doppelkopf is a complex game that involves both individual and team play and requires use of strategic and tactical reasoning, making it a challenging targ
School: Stanford
Course: MACHINE LEARNING
CS 229 Project : Improving on Yelp Reviews Using NLP and Bayesian Scoring Patrick Bechon pbechon@stanford.edu 1. Lo Grimaldi Yacine Merouchi leo.grimaldi@stanford.edu merouchi@stanford.edu INTRODUCTION Yelp allows its users to share reviews of local busin
School: Stanford
Course: MACHINE LEARNING
WHAT MAKES FOR A HIT POP SONG? WHAT MAKES FOR A POP SONG? NICHOLAS BORG AND GEORGE HOKKANEN Abstract. The possibility of a hit song prediction algorithm is both academically interesting and industry motivated. While several companies currently attest to t
School: Stanford
Course: MACHINE LEARNING
Yelp+ : 10 Times More Information per View Sean Choi, Ernest Ryu, Yuekai Sun December 16, 2011 Abstract In this project, we take a dierent approach and use whats called the max-norm. In this project we investigate two machine learning methods, one supervi
School: Stanford
Course: MACHINE LEARNING
Building a Better Tour Experience with Machine Learning Alan Guo, Chanh Nguyen, and Taesung Park 1. INTRODUCTION The motivation of this project is to solve a problem that we currently face working on a project called 27bards, which seeks to revolutionize
School: Stanford
Course: MACHINE LEARNING
What It Takes To Win: A Machine Learning Analysis of the College Football Box Score John Hamann Most advanced analysis of sports focus on predicting the results for the next game based on the results of previous games. For college football, the value of p
School: Stanford
Course: MACHINE LEARNING
Machine Learning Applied to Terrain Classication for Autonomous Mobile Robot Navigation John Rogers, Andrew Lookingbill CS 229 Final Project I. I NTRODUCTION We work on the Stanford AI Lab team for the DARPA-funded Learning Applied to Ground Robotics (LAG
School: Stanford
Course: MACHINE LEARNING
GroupTime: Probabilistic Scheduling Kendra Carattini and Mike Brzozowski Introduction Perhaps one of computer-supported cooperative work (CSCW)s greatest successes of the past decade has been group scheduling. But virtually all major groupware systems ava
School: Stanford
Course: MACHINE LEARNING
Anthony S. Guerrero (SCPD n4361176) CS229 Class Project 12/15/2005 Identification of heterozygous point mutation events in DNA sequencing chromatograms. Introduction. The recent discovery of activating somatic mutations in cancer that correlate with pheno
School: Stanford
Course: MACHINE LEARNING
r e q i d n t e r s s u p i t e h ux e f g t e g e d c g ts v x gr g xw r g e d d w d xw eq i d g x i t e s s e t i t g r e g h t e yg q g e hw s p i x iw st i i t e d c x iw s gq u f e t xw x e e t f e d s sw d r g d e d d w d x w e q i d g x i r e g s
School: Stanford
Course: MACHINE LEARNING
Chest Pain in the Emergency Department: Use of Asymmetric Penalties in Sequential Minimal Optimization with Feature Selection to Improve Clinical Decision Making Accuracy Acknowledgements: I would like to extend my gratitude to Dr. Judd Hollander for prov
School: Stanford
Course: MACHINE LEARNING
Sentence Unit Detection without an Audio Signal William Morgan 1 Introduction and motivation Sentence unit (SU) detection is the task of dividing a sequence of words into individual sentences. SU detection is a close relative of sentence boundary detectio
School: Stanford
Course: MACHINE LEARNING
STAIR Subcomponent: Learning to Manipulate Objects from Simulated Images Justin Driemeyer CS229 Term Project December 15, 2005 Overview For my project, I am working with Ashutosh Saxena on a subcomponent of the STanford AI Robot, i.e. STAIR. One of the go
School: Stanford
Course: MACHINE LEARNING
1 Decoding Cognitive States from fMRI Timeseries Catie Chang catie@stanford.edu CS229 Final Project Report I. Introduction Conventional analysis of functional magnetic resonance imaging (fMRI) data follows a regression-based approach, in which one identie
School: Stanford
Course: MACHINE LEARNING
User Authentication Based On Behavioral Mouse Dynamics Biometrics Chee-Hyung Yoon Department of Computer Science Stanford University Stanford, CA 94305 chyoon@cs.stanford.edu Daniel Donghyun Kim Department of Computer Science Stanford University Stanford,
School: Stanford
Course: MACHINE LEARNING
Semantic Extensions to Syntactic Analysis of Queries Ben Handy, Rohini Rajaraman Abstract We intend to show that leveraging semantic features can improve precision and recall of query results in information retrieval (IR) systems. Nearly all existing IR s
School: Stanford
Course: MACHINE LEARNING
Florin Ratiu CS229 Final Project Reordering Attachment Candidates in the CSLI Dialogue Systems DMT 1. Abstract This paper describes an approach for selecting the best candidate dialogue move in multidevice dialogue systems based on multiple sources of inf
School: Stanford
International Journal of Business and Social Science Vol. 4 No. 5; May 2013 Broadband Services Selection Criteria of Young Users: Exploratory and Confirmatory Factor Analytic Approach *1 Muhammad Sabbir Rahman 2 Md. Nusrate Aziz 3 Murali Raman 4 Md. Mahmu
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International Journal of Business and Social Science Vol. 2 No.10; June 2011 Comparisons of Competing Models between Attitudinal Loyalty and Behavioral Loyalty Cheng, Shih-I Assistant Professor Department of Business Administration, Shu-Te University, Tai
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International Journal of Business and Social Science Vol. 4 No. 11; September 2013 Efficient Customization of Software Applications of an Organization Rajeev Kumar Assistant Professor Department of Business Administration College of Business Kutztown Univ
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International Journal of Business and Social Science Vol. 4 No. 11; September 2013 Importance of Cross-Cultural Empathy in Selling Perspective from Asian Indians living in the U.S. Duleep Delpechitre, PhD Assistant Professor of Marketing University of Lou
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International Journal of Business and Social Science Vol. 4 No. 3; March 2013 A Study on Determining the Factors That Influence the Customer Value in the Fast Casual Restaurants H. Rafet YUNCU, PhD Anadolu University Faculty of Tourism Department of Gastr
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International Journal of Business and Social Science Vol. 2 No. 14 www.ijbssnet.com Ethics and Customer Loyalty: Some Insights into Online Retailing Services Surendra Arjoon (Corresponding author) Senior Lecturer Department of Management Studies The Unive
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International Journal of Business and Social Science Vol. 4 No. 13; October 2013 An Exploratory Study of Customers Perception of Pricing of Hotel Service Offerings in Calabar Metropolis, Cross River State, Nigeria Juliana B. Akaegbu, MBA Department of Bus
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International Journal of Business and Social Science Vol. 2 No. 14 www.ijbssnet.com What Makes Customers Brand Loyal: A Study on Telecommunication Sector of Pakistan Noor-Ul-Ain Nawaz Hailey College of Commerce University of the Punjab, Lahore, Pakistan E
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International Journal of Business and Social Science Vol. 3 No. 21; November 2012 Implications of Service Quality on Customer Loyalty in the Banking Sector. A Survey of Banks in Homabay County, Kenya Arvinlucy Akinyi Onditi Doctoral Student Business Admin
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International Journal of Business and Social Science Vol. 5, No. 6; May 2014 Loyalty Program Factors and How do they affect Customer Behavior Alazzam, Fahad Department of Technology Management School of Engineering University of Bridgeport 126 Park Avenue
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International Journal of Business and Social Science Vol. 4 No. 9; August 2013 Conceptions of Poverty and Wealth in Ghana Christobel Asiedu Department of Social Sciences Louisiana Tech University P. O. Box 9988, Ruston, LA 71272 Vivian A. Dzokoto Departme
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International Journal of Business and Social Science Vol. 2 No. 9 [Special Issue - May 2011] Corporate Governance and Customer Satisfaction Zhe Zhang, Ph.D (Corresponding Author) Assistant Professor Management, Marketing and Administrative Communications
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International Journal of Business and Social Science Vol. 2 No. 11 [Special Issue - June 2011] Students Perspective of Service Quality in Higher Learning Institutions; An evidence Based Approach Mubbsher Munawar khan Ishfaq Ahmed Muhammd Musarrat Nawaz Ha
School: Stanford
International Journal of Business and Social Science Vol. 3 No. 16 [Special Issue August 2012] Assessment of the Importance Level of the Factors Affecting CS according to the Textile Consumers Ikilem Gocek Yesim Iridag Beceren Textile Engineering Departme
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International Journal of Business and Social Science Vol. 3 No. 23; December 2012 Impact of Brand Image, Service Quality and price on customer satisfaction in Pakistan Telecommunication sector Prof. Dr. Muhammad Ehsan Malik1 Muhammad Mudasar Ghafoor2 Hafi
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International Journal of Business and Social Science Vol. 2 No. 16; September 2011 Impact of Customer Satisfaction on Customer Loyalty and Intentions to Switch: Evidence from Banking Sector of Pakistan Faizan Mohsan Hailey College of Commerce, University
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International Journal of Business and Social Science Vol. 3 No. 2 [Special Issue January 2012] THE IMPACT OF CUSTOMER RELATIONSHIP MARKETING ON COSTUMERS' IMAGE FOR JORDANIAN FIVE STAR HOTELS TAREQ N. HASHEM1 Philadelphia University Amman- Jordan Abstract
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International Journal of Business and Social Science Vol. 3 No. 16 [Special Issue August 2012] The Impact of Service Quality, Customer Satisfaction and Loyalty Programs on Customers Loyalty: Evidence from Banking Sector of Pakistan. Samraz Hafeez SZABIST
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International Journal of Business and Social Science Vol. 3 No. 9; May 2012 Customers Financial Needs satisfaction and Self-service Technology Banking: The Case of Automatic Teller Machines (ATMs) in Jordan Fawzi Al Sawalqa Accounting Department; Financia
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International Journal of Business and Social Science Vol. 3 No. 1; January 2012 The Relationships between Service Quality, Satisfaction, and Behavioral Intentions of Malaysian Spa Center Customers Basheer Abbas Al-alak Ghaleb Awad EL-refae Professor of ma
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International Journal of Business and Social Science Vol. 4 No. 17 [Special Issue December 2013] Evolutionary Process Change Factor on Internal Customer Satisfaction in Telecommunication Companies Jordan Sattam Jumah Al-Sardia Othman Yeop Abdullah (Oya) G
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Course: Economic Analysis II
Professor Jay Bhattacharya Spring 2001 Example: Calculating IEPs and Engel Curves Demand II Find the IEP and Engel Curve for a consumer with Recap: last lecture we covered: Income Expansion Paths and Engel curves Inferior and Normal Goods Necessities
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Course: Semiconductor Optoelectronic Devices
1/10/12 EE243 Semiconductor Optoelectronic Devices ! Prof. James Harris! Room 328, Paul Allen Center for Integrated Systems (CISX)! ! Harris@snow.stanford.edu! Web Page - http:/ee.stanford.edu/~harris! (650) 723-9775, (650) 723-4659 fax! Ofce Hours 2: 05
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Course: Infrastructure Project Development
Infrastructure Project Development F13-CEE241A/141A Professor Gary Griggs TA Jorge Gonzalez Class 05 The Planning Phase Study the planning processes used for public sector projects including alternative analyses, project rating and evaluation methods, and
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Introduc)ontoInforma)onRetrieval Introduc)ontoInforma)onRetrieval Informa)onRetrieval Informa)onRetrieval(IR)isndingmaterial(usually documents)ofanunstructurednature(usuallytext) thatsa)sesaninforma)onneedfromwithinlarge collec)ons(usuallystoredoncompute
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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
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
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Course: Parallel Computing Group Projects
WHY PARALLEL COMPUTING? WHY PARALLEL COMPUTING? Parallel computing has existed for a long time but until recently it was a specialized area that concerned only a small fraction of engineers. Nowadays, parallel computing is a dominant player in scientifi
School: Stanford
Course: Parallel Computing Group Projects
CME 213B WINTER 2015 Eric Darve PERFORMANCE METRICS WHY PERFORMANCE METRICS? Understanding the performance of a code is important: to develop efficient code understand the bottlenecks of a code compare algorithms in a meaningful way, e.g., matrix-vecto
School: Stanford
Course: Parallel Computing Group Projects
CME 213B WINTER 2015 Eric Darve ADVANCED MPI DEADLOCKS DEADLOCKS So far, we have covered Send and Recv. These routines are blocking. This means that the program will not progress until the buffer becomes available. In many cases, MPI uses a system buf
School: Stanford
Course: Parallel Computing Group Projects
CME 213B WINTER 2015 Eric Darve USING GIT GIT Git is a version control system. It can be used for several purposes. It allows easily tracking different versions of the code: Either in time: evolution of your code = snapshots of the project at differen
School: Stanford
Course: Parallel Computing Group Projects
CME213B: CUDA Nick Henderson January 15, 2015 1 / 46 Graphics pipeline GPUs are designed to display nice graphics Process involves converting geometric primitives into an image (pixels) to be displayed on a screen Called rendering or rasterization Imp
School: Stanford
Course: Parallel Computing Group Projects
CME 213B WINTER 2015 Eric Darve MPI DISTRIBUTED MEMORY The most common parallel computing setup, in scientific computing, is to have a large number of computing cores connected by a fast network. The idea then is to proceed as follows: Have each node p
School: Stanford
Course: Parallel Computing Group Projects
CME 213B WINTER 2015 Sean Treichler TASK-GRAPH PARALLELISM OUTLINE Quick Introduction Recap: Data-parallel Programming Models Advantages Shortcomings Task-based Parallelism Task Graphs Scheduling Explicit vs. Implicit Graphs 3 QUICK INTRODUCTION
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Course: Parallel Computing Group Projects
CME 213B WINTER 2015 Eric Darve SHARED MEMORY PROCESSOR SCHEMATIC OF A MULTICORE PROCESSOR Model for shared memory machines Comprised of: A number of processors or cores A shared physical memory (global memory) An interconnection network to connect t
School: Stanford
Course: Parallel Computing Group Projects
CME213B: Build and Test Nick Henderson January 29, 2015 1 / 37 Lecture outline Whats the point? want to make working executables and libraries good idea to understand executables and libraries Need to build them many tools all have quirks save time
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Course: Parallel Computing Group Projects
CME 213B WINTER 2015 Eric Darve SYLLABUS PEOPLE Instructors: Eric Darve, ME, ICME; darve@stanford.edu Nick Henderson, ICME; nwh@stanford.edu Office hours are listed on piazza. 3 WEB SITES Class forum: piazza. You may need to register manually. We wil
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Course: DISCRETE MATHEMATICS AND ALGORITHMS
CME 305: Discrete Mathematics and Algorithms 1 Network Flow A network N is a set containing: a directed graph G(V, E); a vertex s V which has only outgoing edges, we call s the source node; a vertex t V which has only incoming edges, we call t the sink
School: Stanford
Course: DISCRETE MATHEMATICS AND ALGORITHMS
CME 305: Discrete Mathematics and Algorithms 1 Global Min-Cut In the previous sections we have used network ow to nd s-t cuts of minimum value (capacity) in a graph. When the capacities are all unit this corresponds to the fewest number of edges which dis
School: Stanford
Course: DISCRETE MATHEMATICS AND ALGORITHMS
CME 305: Discrete Mathematics and Algorithms 1 Graph Sparscation In this section we discuss the approximation of a graph G(V, E) by a sparse graph H(V, F ) on the same vertex set. In particular, we consider any graph with |E| = (n1+ ) edges to be dense; w
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Course: DISCRETE MATHEMATICS AND ALGORITHMS
THE ELECTRICAL RESISTANCE OF A GRAPH CAPTURES ITS COMMUTE AND COVER TIMES Ashok K. Chandra, Prabhakar Raghavan, Walter L. Ruzzo, Roman Smolensky, and Prasoon Tiwari Abstract. View an n-vertex, m-edge undirected graph as an electrical network with unit res
School: Stanford
Course: DISCRETE MATHEMATICS AND ALGORITHMS
BOLYAI SOCIETY MATHEMATICAL STUDIES, 2 Combinatorics, Paul Erds is Eighty (Volume 2) o Keszthely (Hungary), 1993, pp. 146. Random Walks on Graphs: A Survey L. LOVASZ Dedicated to the marvelous random walk of Paul Erds o through universities, continents, a
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Course: DISCRETE MATHEMATICS AND ALGORITHMS
CME 305: Discrete Mathematics and Algorithms 1 Basic Denitions and Concepts in Graph Theory A graph G(V, E) is a set V of vertices and a set E of edges. In an undirected graph, an edge is an unordered pair of vertices. An ordered pair of vertices is calle
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Course: DISCRETE MATHEMATICS AND ALGORITHMS
CME 305: Discrete Mathematics and Algorithms 1 Computation and Intractability In this series of lecture notes, we have discussed several problems for which there exist polynomial time algorithms for producing solutions min-cut, shortest s-t path, whether
School: Stanford
Course: DISCRETE MATHEMATICS AND ALGORITHMS
CME 305: Discrete Mathematics and Algorithms 1 Approximation Algorithms In light of the apparent intractability of the problems we believe not to lie in P, it makes sense to pursue ideas other than complete solutions to these problems. Three standard appr
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Course: DISCRETE MATHEMATICS AND ALGORITHMS
CME 305: Discrete Mathematics and Algorithms 1 Random Walks and Electrical Networks Random walks are widely used tools in algorithm design and probabilistic analysis and they have numerous applications. Given a graph and a starting vertex, select a neighb
School: Stanford
Course: Global Positioning System
Global Navigation Satellite Systems for AA272C, March 2014 1 2014 Per Enge & Frank Van Diggelen Outline Overview of upcoming systems Global: Glonass, Galileo,Beidou Regional: Quasi-zenith satellite system, Indian regional navigation satellite system
School: Stanford
Course: Global Positioning System
Supplement: Pseudoranges and time, trx, ttx AA272C, Winter 2014 2014 Frank van Diggelen & Per Enge 1 Where do pseudoranges come from? ttx trx pr = (trx ttx) But actually: Receiver measures sub-millisecond delay at the correlators this is pr mod 1ms Als
School: Stanford
Course: Global Positioning System
Lectures 6 & 7 How to get expected Pseudoranges: Satellite position computation Kepler, Newton Coordinate frames GPS (almanac & ephemeris) Matlab 2014, van Diggelen & Enge AA272C GPS, Stanford One more day of theory (Lecture 6) before . . we start to get
School: Stanford
Course: Global Positioning System
Lecture 7 How to get expected Pseudoranges: Satellite position computation Kepler, Newton Coordinate frames GPS (almanac & ephemeris) Adjustment for flight time Matlab 2014, van Diggelen & Enge AA272C GPS, Stanford One more day of theory (Lecture 6) befo
School: Stanford
Course: Global Positioning System
L5 The New GPS Signal Stefan Erker, Steffen Thlert, Johann Furthner, Michael Meurer German Aerospace Center (DLR) Institute of Communications and Navigation BIOGRAPHIES Stefan Erker received his diploma degree in Communication Technology at the Technical
School: Stanford
Course: Global Positioning System
Lecture 11 Sensitivity, Non-coherent integration Coherent and noncoherent overview in your text book: 11.3.2 - 11.3.4, 13.4.2, Details in these class notes. external reference: Chapter 6 of: A-GPS; Assisted GPS, GNSS & SBAS, van Diggelen. 2014, Frank van
School: Stanford
Course: Global Positioning System
Lecture 14b Summary GPS Power, Signals & Receivers 2014, Frank van Diggelen, Per Enge AA272C GPS, Stanford 1 Transmitter power & path loss PdB 10log10 P / 4 R2 10log10 P 10log10 4 R 2 P ,dB 10log10 4 R 2 T T T From:Frank van Diggelen, Per Enge 2014, Pe
School: Stanford
Course: Global Positioning System
Lecture 14a Summary GPS Operation and Navigation 2014, van Diggelen & Enge AA272C GPS, Stanford 1 Latitude, Longitude and distance Some useful measures to know: 1 Longitude = 1 Latitude at the equator = cos(Latitude)*1 elsewhere 1 Latitude = 60 minutes =
School: Stanford
Course: Global Positioning System
Lecture 9b High-Sensitivity introduction Coherent and noncoherent overview in your text book: 11.3.2 - 11.3.4, 13.4.2, Details in these class notes. external reference: Chapter 6 of: A-GPS; Assisted GPS, GNSS & SBAS, van Diggelen. 2014, Frank van Diggele
School: Stanford
Course: Global Positioning System
Lecture 9a Receiver Design: Mixers, Ambiguity function Chapter 11 & 12 of text book Signal conditioning Ambiguity function 2014, van Diggelen & Enge AA272C GPS, Stanford 1 2014, van Diggelen & Enge AA272C GPS, Stanford 2 Mixers & IF (Intermediate Freque
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Course: Global Positioning System
Lecture 10 Sensitivity, Long Coherent Integration Coherent and noncoherent overview in your text book: 11.3.2 - 11.3.4, 13.4.2, Details in these class notes. external reference: Chapter 6 of: A-GPS; Assisted GPS, GNSS & SBAS, van Diggelen. 2014, Frank va
School: Stanford
Course: Global Positioning System
Lecture 8 Acquisition search and A-GPS Overview in Chapter 13.4.2 of text book Details in these notes. external reference: Chapter 3 of: A-GPS; Assisted GPS, GNSS & SBAS, van Diggelen. 2014, Frank van Diggelen and Per Enge AA272C GPS, Stanford 1 Overview
School: Stanford
Course: Global Positioning System
Fundamentals of Satellite Navigation Outline Satellites Ground control segment Signals Receivers Pseudoranging Performance: a first look 1 2012, Per Enge and Frank van Diggelen 2012, Per Enge and Frank van Diggelen 2 1 3 2012, Per Enge and Frank van Di
School: Stanford
Course: Global Positioning System
Lecture 5b Receiver Front End Analysis 2014, van Diggelen & Enge AA272C GPS, Stanford 1 C/N0, Carrier to Noise Density Ratio 2014, van Diggelen & Enge AA272C GPS, Stanford 2 Noise equivalent temperature (noise temperature) 2014, van Diggelen & Enge AA2
School: Stanford
Course: Global Positioning System
Lecture 5a Power v1.1 fixed inconsistencies in power of received signal, link budget 2014, van Diggelen & Enge AA272C GPS, Stanford 1 Overview of 2nd half of course 2014, van Diggelen & Enge AA272C GPS, Stanford Baseband Power & Front End Analysis Satel
School: Stanford
Course: Global Positioning System
1/30/14 Signals for the Global Naviga8on Satellite Systems (GNSS) from Chapter 9 of your textbook + link budget material from AA 279 B Outline Signal basics Spectra (an8-jam) Auto-correla8on (for precision & acquisi
School: Stanford
Course: Global Positioning System
Copyright 2014 Per Enge, Ben Segal & Frank van Diggelen Copyright 2014 Per Enge, Ben Segal & Frank van Diggelen 1 Copyright 2014 Per Enge, Ben Segal & Frank van Diggelen DGPS Errors True clock & location Indicated lo
School: Stanford
Course: Global Positioning System
AA272C, Week 3 Least squares, DOPs Matlab, and Multipath van Diggelen 2014 Frank van Diggelen & Per Enge 1 Outline Practical Navigation Review of standard nav equation z G x Coordinates: ECEF and ENU (or NED) DOPs Matlab code Multipath Convergence about
School: Stanford
Course: Global Positioning System
1/14/14 1 1/14/14 2 1/14/14 3 1/14/14 4 1/14/14 5 1/14/14 6 1/14/14 7 1/14/14 8 1/14/14 9 1/14/14 10 1/14/14 11 1/14/14 Appendices & Backups 12 1/14/14 Review (1/4) Review (2/4) 13 1/14/14 Re
School: Stanford
Course: Global Positioning System
1/6/2014 THE JOY OF GPS: OUTLINE How it works (1 slide) cfw_Technology cfw_Science cfw_Culture Transition from elite to masses Simple yet deep and beautiful 1 GPS COMBINES CLASSIC AND MODERN PHYSICS - to 16 C Geocentric universe (Aristotle, Ptolemy,
School: Stanford
Course: Global Positioning System
AA272C, GPS (Global Positioning System) 10 week course Classes: Tue 8 Jan through Thu 14 March Tue & Thu 12:50 pm 2:05 pm, at room . TBD 40% final exam, 30% midterm, 30% homework Required Text Global Positioning System (GPS): Signals, Measurements an
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Course: CONVEX OPTIMIZATION I
Disciplined Convex Programming Stephen Boyd Michael Grant Electrical Engineering Department, Stanford University University of Pennsylvania, 3/30/07 Outline convex optimization checking convexity via convex calculus convex optimization solvers ecient
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Course: Statistical Methods In Engineering And The Physical Sciences
Stat 111 Lecture 5: Intro to Simulations in R 1 Lecture Outline Rs for loop Simulations in R 2 Rs for loop Often times in statistical computing, you would like to repeat some process multiple times: Repeated Sampling Operating on a vector or matrix [
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Course: Statistical Methods In Engineering And The Physical Sciences
Lecture Outline Stat 111 Two most commonly used estimators: X and Inference for a single mean, Lecture 3: Inference, Stat 104 Style p t-based confidence interval for t-test for Inference for a single proportion, p z-test for p z-based confidence
School: Stanford
Course: Statistical Methods In Engineering And The Physical Sciences
Lecture Outline Stat 111 Rs for loop Lecture 5: Intro to Simulations in R Simulations in R 1 2 for loop syntax Rs for loop 2) for statement: The word for, and in parentheses, the range of values that the iterator (here: i) will take on (here: 1:n,iter).
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Course: Statistical Methods In Engineering And The Physical Sciences
Lecture Outline Stat 111 Intro to Estimation Theory Lecture 4: Intro to Estimation and MOM MOM (Method of Moments) 1 2 Examples: Complicated Parameter Estimation Parameter Estimation So far, weve talked about straight-forward parameters (, 2, p) and th
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Course: Statistical Methods In Engineering And The Physical Sciences
1/27/2014 Welcome to Stat 111: Lecture Outline Introduction to Theoretical Statistics (Really: Intro to Statistical Inference) Course logistics and details How is 111 not 110? A few example problems A little R demonstration 1 2 Kevins Contact Info Stat 11
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Course: Statistical Methods In Engineering And The Physical Sciences
Stat 111 Lecture 2: Parameters, Statistics, and Estimators 1 Lecture Outline Parameters, Statistics, and Estimators Sampling, Samples, and Populations R demonstration 2 Statistical Models and Inferences (from last lecture) Statistical model (DeGroot,
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Course: Elliptic Curves In Cryptography
Department of Industrial Engineering & Operations Research IEOR 160 Operations Research I (Fall 2012) Homework 12 Out: Wednesday, Nov 21, 2012 In: Hand in to GSI before 8:00am section on Friday, Nov 30 , 2012 Problem 1 to 4. P459-1,2,3,4 Problem 5. P473-5
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Course: Elliptic Curves In Cryptography
Department of Industrial Engineering & Operations Research IEOR 160 Operations Research I (Fall 2012) Homework 11 Out: Wednesday, Nov 14, 2012 In: Hand in to GSI before lecture on Monday, Nov 26 , 2012 Problem 1 to 5. P430-4,5,6,12,15 Note: the problems a
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Course: Elliptic Curves In Cryptography
Department of Industrial Engineering & Operations Research IEOR 160 Operations Research I (Fall 2012) Homework 10 Out: Wednesday, Nov 7, 2012 In: Hand in to GSI before 8:00am (the starting time of the rst section at 3113 Etch) on Friday, Nov 16 , 2012 Pro
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Course: DECISION ANALYSIS
MS&E252 Decision Analysis I 11/09/2013 Midterm Exam - Solutions MS&E252 Decision Analysis I 11/09/2013 MS&E252 Decision Analysis I 11/09/2013 1) Solution: c Using Deal A and Deal B and applying the substitution rule, we get: 0.6 0.5 $100 0.5 $50 0.5 0.4 0
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Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Midterm Nov 6th, 2007 Midterm Examination MS&E 252: Decision Analysis I Please read the following instructions carefully! 1. This exam is closed book and closed notes, except for a single sheet (1 side). You may use a calculat
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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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STANFORD UNIVERSITY CS 229, Autumn 2011 Midterm Examination Wednesday, November 9, 6:00pm-9:00pm Question Points 1 Generalized Linear Models /15 2 Gaussian Naive Bayes /15 3 Linear Invariance of Logistic Regression /12 4 2-Regularized SVM /18 5 Uniform Co
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Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Solutions to Probabilistic Problems 2001 Practice Final December 6th, 2007 1) Solution: a Alice did not violate any of the rules of actional thought. She stated that the only preference she has is that she goes somewhere from
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Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Midterm Solutions Nov 14th, 2006 Midterm Examination Solutions Grade Distribution 35 1.00 0.90 30 0.80 25 0.70 0.60 20 0.50 15 0.40 10 0.30 0.20 5 0.10 0 -INF - 0 5 - 10 15 - 20 25 - 30 35 - 40 45 - 50 55 - 60 Cumulative 65 -
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Course: AI
CS229 Practice Midterm 1 CS 229, Autumn 2010 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: 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: DECISION ANALYSIS I
MS&E 252 Decision Analysis I HO31 Final Examination: Part I December 3rd, 2002 Final Examination 2001-2002: Part I Please read the following instructions carefully! 1. This exam is closed book and closed notes. You may use one double-sided 8'x11' sheet of
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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 Time Series Analysis
Stat 207 Practice Final Friday June 01, 2012 NAME_ SUID _ Rule: Open Book + a single sheet of notes. There are 4 Pages. Initial Every Page. 1. TRUE/FALSE (write TRUE OR FALSE in full) _ The autocorrelation sequence of an AR(1) model xt = xt-1 + wt is equa
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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
School: Stanford
Course: Theory Of Probability
Stat 310A/Math 230A Theory of Probability Midterm Solutions Andrea Montanari November 1, 2010 The midterm was long! This will be taken into account in the grading. We will assign points proportionally to the number of questions answered (e.g. Problem 1 co
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Course: Dynamic Systems
MS&E201 Dynamic Systems Spring 2007 Professor Edison Tse Page 1 of 14 May 9, 2007 MS&E 201 DYNAMIC SYSTEMS MIDTERM EXAM 75 minutes, total 100 points Open Book. Open Notes. No computers are permitted at the examination. Calculations will be kept as simple
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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
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
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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: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2011 Solutions to Midterm Exam 1 1. (10 points) Multiplying periodic functions Let f (t) and g (t) be periodic functions with period 1 and Fourier series expansions given by n= an ei2nt , f (t) = n= n
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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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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: DECISION ANALYSIS
MS&E 252 Decision Analysis I Midterm Midterm Examination MS&E 252: Decision Analysis I Please read the following instructions carefully! 1. This exam is closed book and closed notes. You may use a calculator and a foreign language dictionary. Please sit i
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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
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Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Midterm Nov 7th, 2006 Midterm Examination MS&E 252: Decision Analysis I Please read the following instructions carefully! 1. This exam is closed book and closed notes, except for a single sheet (1 side). You may use a calculat
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Course: Machine Learning
STANFORD UNIVERSITY CS 229, Autumn 2012 Midterm Examination XXXX RELEASED SOLUTIONS XXXX Question Points 1 Generalized Linear Models /16 2 Ridge Regression /16 3 Naive Bayes /16 4 Kernel Median Regression /16 5 Uniform Convergence /14 6 Short Answers /34
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Course: Compiler
CS143 Summer 2009 Handout 32 CS143 Practice Final August 8, 2009 Exam Facts The final exam is scheduled for Friday, August 14th at 3:30 p.m. in Skilling 191 and 193. Format The final exam will be a 180-minute written exam. The exam is open-note, closed-bo
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Course: Machine Learning
CS229 Practice Midterm Solutions 1 CS 229, Autumn 2010 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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Course: Programming Methodology
CS 106A Handout #37A Aug 12, 2009 Answers to Additional Practice Final Problems Problem Interactors public class InteractorsSample extends GraphicsProgramcfw_ private GLine fwdslash; private GLine backslash; private static final int LINE_WIDTH = 10; priva
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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: 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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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: 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: 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: 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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EE 261 Fourier Transform and Applications March 17, 2011 Handout #21 Final Examination Solutions 1. (15 points) Fourier series. A function f (t) with period 1 has the Fourier series coecients n 1 n<0 2 cn = 0 n=0 1n 2 n>0 These Fourier series coecients
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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 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: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2012 Midterm Exam October 31, 2012 There are ve questions for a total of 85 points. Please write your answers in the exam booklet provided, and make sure that your answers stand out. Dont forget to
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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: Managerial Accounting
Chapter 008, Activity Based Costing: A Tool to Aid Decision-Making 8B)LO7: ABC absorption costing (Appendix LO5: Product and customer margins LO6: Action Analysis (Appendix 8A) LO4: Second-stage allocation Professional Exam Adapted LO2: First-stage alloca
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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: Web Applications
HTML Webusesadocumentcentricapproach(notpixelcentric):"Displaythefollowing document";Nogeneralpurposepixellevelaccess;Toenableapplications,theWeb providesafewspecialfeatures(forms)plustheabilitytomodifythestructureofthe documentontheflyusingJavascript
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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: Machine Learning
CS229 Practice Midterm Solutions 1 CS 229, Autumn 2009 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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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
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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
School: Stanford
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: 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 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: 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: 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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Department of Physics, Stanford University Physics 21 Mechanics and Heat Autumn 2013-14 Page 1 of 11 MIDTERM EXAMINATION 1 Thursday, October 17, 2013 NOTE THAT THIS EXAMINATION HAS 11 PAGES. PLEASE BE SURE YOUR COPY IS COMPLETE. TAs Name SOLUTIONS Print Y
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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
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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: 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: 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: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #13 10/25/2013 Homework Assignment #4 - Solutions Grade Distribution by Question On the next page you will find a breakdown of how well students did on each question. For each question you will see a bar with three dif
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Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #7 10/14/2007 Homework Assignment #3 Due on Thursday Oct. 18th 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 you
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #23 12/7/2007 Homework Assignment #7- Solutions Distinctions These distinctions were prepared by the teaching team and reflect our best belief of the meanings of these terms. A decision diagram shows the structure of a
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Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout # 8 Due 10/17/2013 Homework Assignment #3 Due on Thursday Oct. 17th 2013, at 11:59 pm Assigned Readings: 1. The Foundation of Decision Analysis (Course Reader I): Finish reading chapter 5 to 7 2. Collection of Readings
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
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Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #14 10/27/2013 Homework Assignment #5 Due on Thursday October 31th, 11:59 pm (Updated on October 27th, 10:30 am) Assigned Reading The Foundation of Decision Analysis: Chapter 10 and 11. Distinctions From the class lect
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Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #6 10/4/2013 Homework Assignment #2 Due on Thursday Oct. 10th 11:59 pm Homework Submission Logistics: You can access the MS&E 252 homework submission site from Coursework at http:/coursework.stanford.edu. Click on Subm
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Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #17 11/9/2007 Homework Assignment #6 Due on Thursday November 15th, 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
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Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #23 11/16/2012 Homework Assignment #6 Solutions 60 0 5 10 15 20 25 1 0.9 50 0.8 0.7 40 0.6 0.5 30 0.4 20 0.3 0.2 10 0.1 0 0 Page 1 of 20 HW#6 Solutions MS&E 252 Decision Analysis I Handout #23 11/16/2012 Page 2 of 20 H
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Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #2 9/26/2013 Homework Assignment #1 You are not required to turn in any of this assignment; however, we expect you to have full knowledge of the material included. Reading 1: Foundations of Decision Analysis (Course Re
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Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #22 11/30/2007 Homework Assignment #7 Due on Thursday, December 6th 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
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Course: DECISION ANALYSIS II
MS&E 352 Handout #2 Decision Analysis II January 6th, 2009 Problem Set 0 Due: January 13, 2009 _ This problem set is a gentle tutorial to the beta distribution, which we shall use extensively in this class. You will need Excel to complete the assignments.
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #12 10/31/2007 Homework Assignment #4 - Solutions Students Distribution: 60 1 0.9 50 0.8 0.7 40 0.6 30 0.5 0.4 20 0.3 0.2 10 0.1 0 9.5-10 0-0.5 0.5-1 1-1.5 1.5-2 2-2.5 2.5-3 3-3.5 3.5-4 4-4.5 4.5-5 5-5.5 5.5-6 6-6.5 6.
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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
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: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #21 11/20/2007 Homework Assignment # Solutions #6 Question Distribution: Page 1 of 20 HW#6 Solutions MS&E 252 Decision Analysis I Handout #21 11/20/2007 Student Distribution: 45 40 35 30 0.6 25 0.5 20 0.4 15 0.3 10 5 0
School: Stanford
Course: MODERN APPLIED STATISTICS: LEARNING
Statistics 315a Homework 1, due Wednesday January 29, 2014. ESL refers to the course textbook, and ESL 2.4 refers to exercise 2.4 in ESL. Since the homework assignments count 70% of your nal grade, you must do them on your own. Problem 1 is computing inte
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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
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EE 284 F. Tobagi Autumn 2010-2011 EE284 Homework Assignment No. 1 Topic: Switching Techniques, Network Topologies Handed out: September 21, 2010 Due: September 30, 2010 in class (Previously September 28 but now extended by 2 days) Total Points: 45 ALL WOR
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Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Homework 7 Homework Assignment #7 Due on Thursday November 28th 11:59 pm Homework Submission Logistics: You can access the MSE 252 homework submission site from Coursework at http:/coursework.stanford.edu. Click on Submit Home
School: Stanford
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
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: Convex Optimization I
EE364a, Winter 2013-14 Prof. S. Boyd EE364a Homework 8 solutions 8.16 Maximum volume rectangle inside a polyhedron. Formulate the following problem as a convex optimization problem. Find the rectangle R = cfw_x Rn | l x u of maximum volume, enclosed in a
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I 9/26/2013 Homework #1 - Solutions Responsible means we have the ability to choose our response to environmental stimuli. Proactive is the recognition that we are responsible for our own lives. We are where we are today because
School: Stanford
Course: Circuits I
EE101A/Winter 2013 Prof. Simon Wong Homework #2 (Due Wednesday, 1/23/13) 1. Determine the equivalent resistance measured between the two terminals if all resistors are 1K. (This is a 2D hexagon, NOT a 3D cube.) R =? 2. Use Nodal Analysis to determine the
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: LINEAR AND NON-LINEAR OPTIMIZATION
MS&E 211 Linear and Nonlinear Optimization Prof. Yinyu Ye Fall 2007 Oct 9, 2007 Homework Assignment 2: Sample Solution Problem 1 (a) Let p1 , p2 , p3 be the number of production cycles that Process 1, Process 2 and Process 3 finish, respectively. Then, th
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
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: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #15 11/4/2007 Homework Assignment #5 Solutions Students Distribution: 40 1 0.9 35 0.8 30 0.7 25 0.6 20 0.5 0.4 15 0.3 10 0.2 5 0.1 0 3-3.5 6.5-7 8-8.5 9.5-10 1.5-2 0-0.5 1-1.5 3.5-4 4.5-5 5-5.5 5.5-6 6-6.5 0.5-1 2.5-3
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
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: MODERN APPLIED STATISTICS: LEARNING
Stats 315A HW1 Solutions February, 2014 Grade Distribution: Total 100 Points Problem 1: 30 [6 + 8 + 8 + 8] Problem 2: 10 Problem 3: 15 [3 + 5 + 4 + 3] Problem 4: 20 [17 + 3] Problem 5: 10 Problem 6: 15 [4 + 3 + 4 + 4] Problem 1 Part (a): The code is provi
School: Stanford
Stanford University Management Science and Engineering Professor Chang & Timucin MS&E 260 Fall 2013/14 MS&E 260: INTRODUCTION TO OPERATIONS MANAGEMENT HOMEWORK #1 Solutions 1. (40 pts.) (a) (15 pts.) DECISION VARIABLES: yk: 1 if transfer station k is sele
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #10 11/7/2013 Homework Assignment #3 - Solutions Grade Distribution by Question On the next page you will find a breakdown of how well students did on each question. For each question you will see a bar with three diff
School: Stanford
Course: Digital MOS Integrated Circuits
EE313 Winter 2009-10 J. Kim & M. Horowitz page 1 of 8 SOLUTIONS TO HOMEWORK #2 1. Logical Effort simulations (20 points) The spice deck and Virtuoso schematics /usr/class/ee313/HW2/sol. for this problem can be found in: Delay is measured as the average of
School: Stanford
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
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: Introduction To Optimization
e62 Introduction to Optimization Prof. Ben Van Roy Spring 2008 April 2, 2008 Homework Assignment 1: Due April 11 Network Routing The purpose of this problem is to develop experience with Excel Solver. We will be working with the spreadsheet presented on t
School: Stanford
Course: Integrated Circuit Fabrication Processes
EE 212 FALL 09-10 HOMEWORK ASSIGNMENT #3 ASSIGNED: THURSDAY OCT. 15 DUE: THURSDAY OCT. 22 SOLUTION SHEET #1. An experimental DUV resist has a contrast of 5. It is being used with a projection imaging system that produces the aerial image shown below. Will
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
Assignment 6: calculating VaR 1. It is October 2011, and you are working in the risk management division of the publicly traded company Pear Inc. Pears core business units are the production of computers, mobile phones, and tablets. Nevertheless, Pear Inc
School: Stanford
Stanford University Management Science and Engineering Professor Chang & Timucin MS&E 260 Fall 2013/14 MS&E 260: Introduction to Operations Management HOMEWORK #3 Due on Monday, October 21st, 2:15pm 1. (25 pts.) A racing bike manufacturer called XMB is us
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2012 Solutions to Problem Set Four 1. (10 points) Solving the wave equation An innite string is stretched along the x-axis and is given an initial displacement described by a function f (x). It is the
School: Stanford
Course: Digital MOS Integrated Circuits
EE313 Winter 09/10 J. Kim & M. Horowitz Handout # Page 1 of 6 HOMEWORK #2 (Due: Wednesday Jan. 27th, 2010; in class) 1. Logical Effort simulations In this problem, you will use HSPICE to measure the logical effort of a few different kinds of gates. Accord
School: Stanford
Course: Stochastic Modeling
MS&E 221 CA: Erick Delage Problem Set 2 Solutions January 31, 2007 Problem 1 First, well identify the communicating classes then for each class we want to check if it is closed or not. Since the classes will be nite for all the examples below, the class w
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Course: DECISION ANALYSIS II
MS&E 352 Handout #25 Decision Analysis II March 11th, 2009 _ Problem Set #4 - Solutions Grade Distribution 35% 30% 25% 20% 15% 10% 5% 0% 16-20 21-25 46-50 51-55 56-60 86-90 91-95 26-30 31-35 36-40 41-45 61-65 66-70 71-75 76-80 81-85 96-100 11-15 0-5 6-10
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2012 Problem Set Eight Due Wednesday, November 28 1. (20 points) A True Story : Professor Osgood and a graduate student were working on a discrete form of the sampling theorem. This included looking a
School: Stanford
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 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: Probabilistic Analysis
HOMEWORK #3 SOLUTIONS Chapter 5 Exercise 15 a) P(A wins)=(1/9+1/9*P(A wins)*3 because there are three symmetrical path from the regeneration point (tie). Therefore P(A wins)=1/2 A wins A rock B wins Tie rock A wins Start A paper B wins Tie paper A wins A
School: Stanford
Course: Stochastic Control
EE365, Spring 2011-12 Professors S. Boyd, S. Lall, and B. Van Roy EE365 / MS&E251 Homework 5 Solutions 1. A rened inventory model. We consider an inventory model that is more rened than the one youve seen in the lectures. The amount of inventory at time t
School: Stanford
Course: Stochastic Control
EE365, Spring 2011-12 Professors S. Boyd, S. Lall, and B. Van Roy EE365 / MS&E251 Homework 5 1. A rened inventory model. We consider an inventory model that is more rened than the one youve seen in the lectures. The amount of inventory at time t is denote
School: Stanford
Course: Introductory Economics A
Problem Set 1 Hard copies of the answers to these questions are due at the beginning of your section, either on Thursday, October 1, or Friday, October 2. For example, if your section starts at 10:00am on Friday, you should submit your answers to your tea
School: Stanford
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
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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: 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: 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
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
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Programming Abstractions
Eric Roberts CS 106B Handout #32 February 9, 2015 Section Handout #5 The EditorBuffer Class Problem 1. The gap-buffer form of the stack model (Chapter 13, exercise 2, page 610) Even though the stacks in the stackbuf.cpp implementation of the EditorBuffer
School: Stanford
Course: Programming Abstractions
Eric Roberts CS 106B Handout #32A February 9, 2015 Solutions to Section Handout #5 Problem 1. Implementing the array-with-gap form of the two stack model 2 3 4 Problem 2: Doubly linked lists 5 6
School: Stanford
Course: Programming Abstractions
Eric Roberts CS 106B Handout #38 February 18, 2015 Section #6Trees For problems 1, 2, and 3, assume that BSTNode is defined as follows: struct BSTNode cfw_ string key; BSTNode *left, *right; ; 1. Tracing binary tree insertion (Chapter 16, review question
School: Stanford
Course: Data Mining And Analysis
Sheet1 V1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V3
School: Stanford
Course: Data Mining And Analysis
Sheet1 V1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V3
School: Stanford
Course: Data Mining And Analysis
Sheet1 V1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V3
School: Stanford
Course: Data Mining And Analysis
Sheet1 V1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V3
School: Stanford
Course: Data Mining And Analysis
Sheet1 V1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V3
School: Stanford
Course: Elliptic Curves In Cryptography
lab10sol November 10, 2014 1 Virtual Lab 10 Solution: Biased Coin 1.0.1 EECS 70: Discrete Mathematics and Probability Theory, Fall 2014 Due Date: Monday, November 10th, 2014 at 12pm Login: cs70-ta Instructions: Name: EECS 70 Please ll out your name and l
School: Stanford
Course: Elliptic Curves In Cryptography
lab9sol November 2, 2014 1 Virtual Lab 9 Solution: Intro to Randomness (cont.) 1.0.1 EECS 70: Discrete Mathematics and Probability Theory, Fall 2014 Due Date: Monday, November 3rd, 2014 at 12pm Login: cs70Instructions: 1.1 Name: Please ll out your name an
School: Stanford
Course: Elliptic Curves In Cryptography
lab8sol October 27, 2014 1 Virtual Lab 8 Solution: Intro to Randomness 1.0.1 EECS 70: Discrete Mathematics and Probability Theory, Fall 2014 Due Date: Monday, October 27th, 2014 at 12pm Login: cs70Instructions: 1.1 Name: Please ll out your name and login
School: Stanford
Course: Elliptic Curves In Cryptography
lab7sol October 13, 2014 1 Virtual Lab 7 Solution: Polynomials, Secret Sharing, and Histograms 1.0.1 EECS 70: Discrete Mathematics and Probability Theory, Fall 2014 Due Date: Monday, October 20th, 2014 at 12pm Login: cs70-ta Instructions: Name: EECS 70 P
School: Stanford
Course: Elliptic Curves In Cryptography
lab6sol October 9, 2014 1 Virtual Lab 6 Solution: Public Key Cryptography and Lagrange Interpolation 1.0.1 EECS 70: Discrete Mathematics and Probability Theory, Fall 2014 Due Date: Monday, October 13th, 2014 at 12pm Instructions: Complete this lab by lli
School: Stanford
Course: Elliptic Curves In Cryptography
VirtualLab5Solution:ChineseRemainderTheoremand Euler'sTheorem EECS70:DiscreteMathematicsandProbabilityTheory,Fall2014 DueDate:Monday,October6th,2014at12pm Instructions: Completethislabbyfillinginalloftherequiredfunctions,markedwith" O R C D H R " YU OE EE
School: Stanford
Course: Elliptic Curves In Cryptography
lab14sol December 8, 2014 1 Virtual Lab 14 Solution: Random Variables and Distributions 1.0.1 EECS 70: Discrete Mathematics and Probability Theory, Fall 2014 Due Date: Monday, December 8th, 2014 at 12pm Login: cs70Instructions: Name: Please ll out your n
School: Stanford
Course: Elliptic Curves In Cryptography
lab12sol November 17, 2014 1 Virtual Lab 12 Solution: Hashing & Drunk Man 1.0.1 EECS 70: Discrete Mathematics and Probability Theory, Fall 2014 Due Date: Monday, November 24th, 2014 at 12pm Login: cs70-ta Instructions: Name: EECS 70 Please ll out your na
School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Applied Mechanics: Statics
Option 1 2 3 4 5 Manufacturer Chainring Cassette Crank Length Speed Ratio Tourist Pedal Commuter Pedal Shopper Pedal Mechanical Advantage Brand teeth teeth mm Equation 1 RPM RPM RPM Equation 2 (8) (9) (10) (11) (12) (13) (14) (15) (16) Shimano 30 28 170 T
School: Stanford
Course: Applied Mechanics: Statics
ENGR-14: Solid Mechanics Case Study Series Mark Schar and Ruben Pierre-Antoine Trek B-cycle Designing a Drive Train and starting in January 2008, Boulder will be the most bicycle friendly city in the United States! proclaimed Mayor Shaun McGrath. With th
School: Stanford
Course: Applied Mechanics: Statics
ENGR-14: Solid Mechanics Case Study Series Mark Schar and Mark Cuson Madison Longboard Designing a Deck Watch out, Phil Knight. Here we come! said Adam in a mock menacing tone. Yeah, sure replied Sam. Weve got a long way to go before anyones comparing us
School: Stanford
Course: Biology Of Birds
APBiology Unit3Genetics Name: Period: Date: HardyWeinbergEquilibriumPracticeProblems UseaftercompletingtheMasteringBiologyactivity Forallofthefollowingproblems,assumethatthepopulationsareinHardyWeinbergequilibrium,unless itisnotedotherwise. 1.Givenapopula
School: Stanford
Course: Biology Of Birds
AP Biology Chapter 18 and 19 1. Feedback inhibition is a recurring mechanism throughout biological systems. In the case of E. coli regulating tryptophan synthesis, is it positive or negative inhibition? Explain your choice. 2. Compare and contrast the lac
School: Stanford
Course: Applied Multivariate Analysis
Statistics 305: Linear Models Introduction to R: Page 1 Autumn Quarter 2011 What is R? R is a statistics package freely available at http:/www.r-project.org/. Since R is a programming language, it is exible but that comes with the price of a somewhat stee
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
School: Stanford
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
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
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
Course: Problem Solving For CS Technical Interview
FundamentalAlgorithmsandDataStructures Worktogetherinagrouptoanswereachofthefollowingquestions.Werecommendusingthese questionsasstudypracticegoingforward. DataStructures 1. Whatisthetimecomplexityofaddinganelementtotheendofadynamicarray?Whatis thetimecomp
School: Stanford
Course: Randomized Algorithms
Discrete Mathematics and Algorithms ICME Refresher Course Austin Benson September 15, 2014 These are the lecture notes for the ICME summer 2014 refresher course on discrete mathematics and algorithms. The material is meant to be preparatory for CME 305, t
School: Stanford
Course: Statistical Methods In Engineering And The Physical Sciences
IEOR 153 Supply Chain Management and Logistics Network Design Spring, 2014 Instructor: Prof. Rob Leachman Office: 4127 Etcheverry Hall Phone: 642-7054 E-mail: leachman@ieor.berkeley.edu Office hours: MW 2-3 or by appointment Course meetings: MWF 10-11 in
School: Stanford
Course: Convex Optimization I
Additional Exercises for Convex Optimization Stephen Boyd Lieven Vandenberghe January 10, 2014 This is a collection of additional exercises, meant to supplement those found in the book Convex Optimization, by Stephen Boyd and Lieven Vandenberghe. These ex
School: Stanford
Course: Fundamentals Of Analog Integrated Circuit Design
EE114/ 214A Review Session 2 Simon Basilico and Yaoyu Tao Stanford University taoyaoyu@stanford.edu basilico@stanford.edu A. Arbabian, R. Dutton, B. Murmann EE 114/214A 1 Important Announcements Start HW2 as soon as possible as it requires HSpice setup a
School: Stanford
Course: Fundamentals Of Analog Integrated Circuit Design
EE114/ 214A Review Session 1 Jayant Charthad Stanford University jayantc@stanford.edu A. Arbabian, R. Dutton, B. Murmann EE 114/214A 1 Important Announcements Please make sure you are enrolled on the course website and you are getting course announcement
School: Stanford
Course: Supply Chain Management
Supply Chain Performance Measurement and Financial Analysis Instructors Manual Chapter 5 CHAPTER 5 SUPPLY CHAIN PERFORMANCE MEASUREMENT AND FINANCIAL ANALYSIS LEARNING OBJECTIVES After reading this chapter, you should be able to do the following: Underst
School: Stanford
Course: Supply Chain Management
Strategic Challenges and Change for Supply Chains Instructors Manual Chapter 16 CHAPTER 16 STRATEGIC CHALLENGES AND CHANGE FOR SUPPLY CHAINS LEARNING OBJECTIVES After reading this chapter, you should be able to do the following: Understand current and fu
School: Stanford
Course: Supply Chain Management
Managing Reverse Flows in the Supply Chain Instructors Manual Chapter 15 CHAPTER 15 MANAGING REVERSE FLOWS IN THE SUPPLY CHAIN LEARNING OBJECTIVES After reading this chapter, you should be able to do the following: Understand why reverse flows in the sup
School: Stanford
Course: Supply Chain Management
OperationsProducing Goods and Services Instructors Manual Chapter 14 CHAPTER 14 OPERATIONSPRODUCING GOOD AND SERVICES LEARNING OBJECTIVES After reading this chapter, you should be able to do the following: Discuss the strategic value-adding role operatio
School: Stanford
Course: Supply Chain Management
Sourcing Materials and Services Instructors Manual Chapter 13 CHAPTER 13 SOURCING MATERIALS AND SERVICES LEARNING OBJECTIVES After reading this chapter, you should be able to do the following: Understand the role and nature of purchasing, procurement, an
School: Stanford
Course: Supply Chain Management
DistributionManaging Fulfillment Operations Instructors Manual Chapter 11 CHAPTER 11 DISTRIBUTIONMANAGING FULFILLMENT OPERATIONS LEARNING OBJECTIVES After reading this chapter, you should be able to do the following: Discuss the strategic value-adding ro
School: Stanford
Course: Supply Chain Management
Supply Chain Network Analysis and Design Instructors Manual Chapter 12 CHAPTER 12 SUPPLY CHAIN NETWORK ANALYSIS AND DESIGN LEARNING OBJECTIVES After reading this chapter, you should be able to do the following: Understand the critical need in certain com
School: Stanford
Course: Supply Chain Management
Managing Inventory in the Supply Chain Instructors Manual Chapter 9 CHAPTER 9 MANAGING INVENTORY IN THE SUPPLY CHAIN LEARNING OBJECTIVES After reading this chapter, you should be able to do the following: Appreciate the role and importance of inventory i
School: Stanford
Course: Supply Chain Management
TransportationManaging the Flow of the Supply Chain Instructors Manual Chapter 10 CHAPTER 10 TRANSPORTATIONMANAGING THE FLOW OF THE SUPPLY CHAIN LEARNING OBJECTIVES After reading this chapter, you should be able to do the following: Explain the role tran
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Course: Supply Chain Management
Supply Chain Management: An Overview Instructors Manual Chapter 1 CHAPTER 1 SUPPLY CHAIN MANAGEMENT: AN OVERVIEW LEARNING OBJECTIVES The major objectives of this chapter are: Discuss the major change drivers in our economy and in the global marketplace.
School: Stanford
Course: Supply Chain Management
Order Management and Customer Service Instructors Manual Chapter 8 CHAPTER 8 ORDER MANAGEMENT AND CUSTOMER SERVICE LEARNING OBJECTIVES After reading this chapter, you should be able to do the following: Understand the relationships between order manageme
School: Stanford
Course: Supply Chain Management
Demand Management Instructors Manual Chapter 7 CHAPTER 7 DEMAND MANAGEMENT LEARNING OBJECTIVES After reading this chapter, you should be able to do the following: Understand the critical importance of outbound-to-customer logistics systems. Appreciate t
School: Stanford
Course: Supply Chain Management
Supply Chain TechnologyManaging Information Flows Instructors Manual Chapter 6 CHAPTER 6 SUPPLY CHAIN TECHNOLOGYMANAGING INFORMATION FLOWS LEARNING OBJECTIVES After reading this chapter, you should be able to do the following: Appreciate the overall impo
School: Stanford
Course: Supply Chain Management
Supply Chain Relationships Instructors Manual Chapter 4 CHAPTER 4 SUPPLY CHAIN RELATIONSHIPS LEARNING OBJECTIVES After reading this chapter, you should be able to do the following: Understand the types of supply chain relationships and their importance.
School: Stanford
Course: Supply Chain Management
Global Dimensions of Supply Chains Instructors Manual Chapter 3 CHAPTER 3 GLOBAL DIMENSIONS OF SUPPLY CHAINS LEARNING OBJECTIVES After reading this chapter, you should be able to do the following: Describe the scope of a global companys supply chain netw
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Course: Supply Chain Management
Role of Logistics in Supply Chains Instructors Manual Chapter 2 CHAPTER 2 ROLE OF LOGISTICS IN SUPPLY CHAINS LEARNING OBJECTIVES After reading this chapter, you should be able to do the following: Understand the role and importance of logistics in privat
School: Stanford
Course: Strategic Management
C09-10-0011 Mary B. Teagarden Andreas Schotter Michael Greto Toyota: The Accelerator Crisis Teaching Note Case Synopsis Toyota, the worlds leading automotive company and a global benchmark for quality and continuous improvement, stumbled seriously. They f
School: Stanford
Course: Strategic Management
The CASE Journal Volume 6, Issue 1 (Fall 2009) www.caseweb.org THE UNTSIYA COMPANY: BUSINESS DEVELOPMENT IN RUSSIA Galina Shirokova St. Petersburg State University (Russia) Gina Vega Salem state College 2010 The CASE Journal ecch the case for learning Di
School: Stanford
Course: Strategic Management
WHOLE FOOD MARKETS (2010): HOW TO GROW IN AN INCREASINGLY COMPETITIVE MARKET? CASE ABSTRACT Whole Foods Market was the world's leading retailer of natural and organic foods, with 289 stores (2010) in North America and the United Kingdom. The supply of nat
School: Stanford
Course: Strategic Management
S w 8B10M89 Teaching Note VEJA: SNEAKERS WITH A CONSCIENCE Kim Poldner wrote this teaching note under the supervision of Professor Oana Branzei as an aid to instructors in the classroom use of the case Veja: Sneakers With A Conscience, No. 9B10M089. This
School: Stanford
Course: Strategic Management
Wynn Resorts, Ltd. 2008 Teaching Note Victoria Page and Alan Hoffman Bentley College 1. What is the importance of Mr. Steven Wynn himself, to the company? What potential problems could arise if he left the company? Very few people win big, and even fewer
School: Stanford
Course: Strategic Management
THE APOLLO GROUP (University of Phoenix) Case Instructors Guide Case Summary This case provides a description of the University of Phoenix and three other educational businesses that comprise The Apollo Group, Inc. It is an exciting story about an educati
School: Stanford
Course: Strategic Management
Wells Fargo Teaching Notes I. Current Situation A. Current Performance Revenue increased by 61.25%[US$34,898 million, 2008 to US$56,274 million in 2009] Wachovia acquired end of 2008 In 2009, 72% of revenue attributed to community Banking. Net income in 2
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Course: Strategic Management
S w 8B10A12 Teaching Note THE ULTIMATE FIGHTING CHAMPIONSHIPS (UFC): THE EVOLUTION OF A SPORT Jesse Baker wrote this teaching note under the supervision of Matthew Thomson as an aid to instructors in the classroom use of the case The Ultimate Fighting Cha
School: Stanford
Course: Strategic Management
TCJ06-02-04TN PHILOSOPHERS WOOL CO.: SME SUSTAINABLE SUPPLY CHAIN MANAGEMENT IN THE GLOBAL ECONOMY Miriam F. Weismann, University_ Suffolk Case Synopsis Initially, Philosophers Wool Company, located in Inverhuron Canada, was operated as a wholesale distri
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Course: Strategic Management
TCJ06-02-01TN Competing for New Yorks Best Lobster Roll: Failed Trade Protection David E. Desplaces, College of Charleston Roxane M. Delaurell, College of Charleston Laquita C. Blockson, College of Charleston_ Case Synopsis Pearl Oyster Bar, founded in 19
School: Stanford
Course: Strategic Management
UVA-QA-0749TN Oct. 21, 2010 SALMONES PUYUHUAPI (A), (B), AND (C) Teaching Note Synopsis Osvaldo Correa, CEO of Salmones Puyuhuapi (SP), must decide how to respond to news that the ISA virus has infected a competitors salmon farming site in the Jacaf Fjord