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School: Pittsburgh
Course: Introduction To Computer Graphics
cs1566 Homework 2 (part 1) Stitcher Algo CS1566 Assignment 2 (part 1) Solutions Stitcher Algorithm Problem 1 Your boss at Acme Graphics Corp. wants you to write a procedure to first compute and then render a sphere in OpenGL. His evil plan is to mutilate
School: Pittsburgh
Course: Advanced Topics In Artificial Intelligence
CS 3710 Advanced Topics in AI Lecture 18 Density estimation Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 3710 Probabilistic graphical models Density estimation Data: D = cfw_D1 , D2 ,., Dn Di = x i a vector of attribute values Objective: try
School: Pittsburgh
Course: Advanced Topics In Artificial Intelligence
Mean Field / Variational Approximations Presented by Jose Nuez 10/24/05 Outline Introduction Mean Field Approximation Structured Mean Field Weighted Mean Field Variational Methods Introduction Problem: We have distribution P(x) but inference is hard to
School: Pittsburgh
Course: Advanced Topics In Artificial Intelligence
CS 3710 Advanced Topics in AI Lecture 19 Learning Bayesian belief networks Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 3710 Probabilistic Graphical Models Learning probability distribution Basic settings: A set of random variables X = cfw_ X
School: Pittsburgh
Course: Advanced Topics In Artificial Intelligence
A Differential Approach to Inference in Bayesian Networks Presented by Yanna Shen shenyn@cbmi.pitt.edu 1 Outline Introduction Overview of algorithms for inference in Bayesian networks (BN) Proposed new approach How to represent BN as multi-variate polynom
School: Pittsburgh
Course: Advanced Topics In Artificial Intelligence
CS 3710 Advanced Topics in AI Lecture 15 Constructing Free Energy Approximations and GBP Algorithms Branislav Kveton bkveton@cs.pitt.edu 5802 Sennott Square CS 3710 Probabilistic graphical models Content Why? Belief propagation (BP) Factor graphs Region-
School: Pittsburgh
Course: Advanced Topics In Artificial Intelligence
CS 3710 Advanced Topics in AI Lecture 17 Density estimation Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 3710 Probabilistic graphical models Administration Midterm: A take-home exam (1 week) Due on Wednesday, November 2, 2005 before the class
School: Pittsburgh
Course: Design & Analysis Of Algorthms
CS 1510 Midterm 2 Fall 2010 1. (20 points) Consider the following two problems: TRAVELING SALESMAN INPUT: points (x1 , y1 ), . . . (xn , yn ) in the Euclidean plane OUTPUT: The shortest route, starting from the origin, that visits all the points. You can
School: Pittsburgh
Course: Design & Analysis Of Algorthms
CS 1510 Midterm 1 Fall 2011 1. (40 points) Consider the following problem. The input is a collection A = cfw_a1 , . . . , an of n points on the real line. The problem is to nd a minimum cardinality collection S of unit length intervals that cover every p
School: Pittsburgh
Course: Design & Analysis Of Algorthms
CS 1510 Midterm 2 Fall 2010 The test is a bit long. I suggest keeping your answers short and to the point. 1. (a) (5 points) Consider the standard EREW algorithm for adding n integers that runs in time T (n, p) = n/p + log p with p processors. Dene the ec
School: Pittsburgh
Course: Design & Analysis Of Algorthms
CS 1510 Midterm 1 Fall 2010 1. (40 points) We consider the following scheduling problem: INPUT: A collection of jobs J1 , . . . , Jn , where the ith job is a tuple (ri , xi ) of non-negative integers specifying the release time and size of the job. OUTPUT
School: Pittsburgh
Course: Design & Analysis Of Algorthms
CS 1510 Midterm 2 Fall 2009 The test is a bit long. I suggest keeping your answers short and to the point. 1. (a) (5 points) According to your instructor, what is the most important reason that multiplicative constants are not taken into account when comp
School: Pittsburgh
Course: Design & Analysis Of Algorthms
CS 1510 Midterm 1 Fall 2009 1. (40 points) We consider the following problem: INPUT: A collection W of positive integer weights w1 , . . . , wn. OUTPUT: A binary tree T with n leaves, where each leaf is labeled with a unique weight w() from W , that minim
School: Pittsburgh
Course: Introduction To Computer Graphics
cs1566 Homework 2 (part 1) Stitcher Algo CS1566 Assignment 2 (part 1) Solutions Stitcher Algorithm Problem 1 Your boss at Acme Graphics Corp. wants you to write a procedure to first compute and then render a sphere in OpenGL. His evil plan is to mutilate
School: Pittsburgh
Course: Knowledge Representation
University of Pittsburgh CS 2740 Knowledge Representation Professor Milos Hauskrecht Handout 15 December 5, 2008 Problem set 6 solutions Uncertainty Problem 1 Let A,B,C be random variables. A and B have two possible values T,F and C has three values: high
School: Pittsburgh
Course: Knowledge Representation
University of Pittsburgh CS 2740 Knowledge Representation Professor Milos Hauskrecht Handout 9 October 15, 2008 Solutions to problem set 4 Problem 1. Unication See example solution on the course web site. Problem 2. Inference in FOL Let L be a rst-order l
School: Pittsburgh
Course: Knowledge Representation
University of Pittsburgh CS 2740 Knowledge Representation Professor Milos Hauskrecht Handout 14 December 5, 2008 Problem set 5 solutions STRIPS Planning Problem 1 Consider a simple blocks world problem: C B A C Initial state D Goal state B A D We use pred
School: Pittsburgh
Course: Knowledge Representation
University of Pittsburgh CS 2740 Knowledge Representation Professor Milos Hauskrecht Handout 16 December 5, 2008 Problem set 7 solutions Uncertainty. Problem 1. Bayesian Belief Networks. Assume the Bayesian belief network for the diagnosis of cars electir
School: Pittsburgh
Course: Knowledge Representation
University of Pittsburgh CS 2740 Knowledge Representation Professor Milos Hauskrecht Handout 5 October 1, 2008 Problem assignment 4 Due: Wednesday, October 8, 2008 Unication The unication process in FOL aims to nd the most general substitution that makes
School: Pittsburgh
Course: Intro To CS
3: C 3: C , C, Dev-C+, Windows gcc C Unix. , WinSCP Windows ( )
School: Pittsburgh
Course: Introduction To Computer Graphics
cs1566 Homework 2 (part 1) Stitcher Algo CS1566 Assignment 2 (part 1) Solutions Stitcher Algorithm Problem 1 Your boss at Acme Graphics Corp. wants you to write a procedure to first compute and then render a sphere in OpenGL. His evil plan is to mutilate
School: Pittsburgh
Course: Advanced Topics In Artificial Intelligence
CS 3710 Advanced Topics in AI Lecture 18 Density estimation Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 3710 Probabilistic graphical models Density estimation Data: D = cfw_D1 , D2 ,., Dn Di = x i a vector of attribute values Objective: try
School: Pittsburgh
Course: Advanced Topics In Artificial Intelligence
Mean Field / Variational Approximations Presented by Jose Nuez 10/24/05 Outline Introduction Mean Field Approximation Structured Mean Field Weighted Mean Field Variational Methods Introduction Problem: We have distribution P(x) but inference is hard to
School: Pittsburgh
Course: Advanced Topics In Artificial Intelligence
CS 3710 Advanced Topics in AI Lecture 19 Learning Bayesian belief networks Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 3710 Probabilistic Graphical Models Learning probability distribution Basic settings: A set of random variables X = cfw_ X
School: Pittsburgh
Course: Advanced Topics In Artificial Intelligence
A Differential Approach to Inference in Bayesian Networks Presented by Yanna Shen shenyn@cbmi.pitt.edu 1 Outline Introduction Overview of algorithms for inference in Bayesian networks (BN) Proposed new approach How to represent BN as multi-variate polynom
School: Pittsburgh
Course: Advanced Topics In Artificial Intelligence
CS 3710 Advanced Topics in AI Lecture 15 Constructing Free Energy Approximations and GBP Algorithms Branislav Kveton bkveton@cs.pitt.edu 5802 Sennott Square CS 3710 Probabilistic graphical models Content Why? Belief propagation (BP) Factor graphs Region-
School: Pittsburgh
Course: Advanced Topics In Artificial Intelligence
CS 3710 Advanced Topics in AI Lecture 17 Density estimation Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 3710 Probabilistic graphical models Administration Midterm: A take-home exam (1 week) Due on Wednesday, November 2, 2005 before the class
School: Pittsburgh
Course: Advanced Topics In Artificial Intelligence
CS 3710 Advanced Topics in AI Lecture 14 Approximations as optimizations (Chapter 10) Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 3710 Probabilistic graphical models Approximation as an optimization Idea: Assume we have the distribution P(x)
School: Pittsburgh
Course: Advanced Topics In Artificial Intelligence
Particle-Based Approximate Inference using Random Sampling Presented by Hua Ai 09/26/2005 Modified by milos 10/15/05 1 Particles Particles: a set of instantiations of joint distribution to all or some of the variables in the network 2 Outline Forward Samp
School: Pittsburgh
Course: Advanced Topics In Artificial Intelligence
Bayesian Networks Representation Dave Essary Overview Distributions, graphs, and independences I-MAPs d-separation (d-connection) I-equivalence P-MAP and equivalence classes 1 Independences Distribution: a set of probabilities of a random variables repres
School: Pittsburgh
Course: Advanced Topics In Artificial Intelligence
CS 3710 Advanced Topics in AI Lecture 2 Probabilistic graphical models Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square, x4-8845 http:/www.cs.pitt.edu/~milos/courses/cs3710/ CS 3710 Probabilistic Graphical Models Motivation. Medical example. We want
School: Pittsburgh
Course: Advanced Topics In Artificial Intelligence
Markov Chain Monte Carlo Methods Presented by Chang Liu CS3710 2005.9.28 Outline Markov Chain Gibbs Sampling Build a Markov Chain Mixing Time in Using Markov Chain 1 Why Markov Chain? P(X|e) e1 e2 P(X|e) the query we want to compute e1 & e2 are known evid
School: Pittsburgh
Course: Advanced Topics In Artificial Intelligence
Cluster trees and message propagation CS3710 Advanced AI Tomas Singliar Outline Simple graphs: trees and polytrees Cluster graphs and clique trees running intersection, sepsetsMessage propagation ( = VE ) Message passing VE in detail Caching, out-of-cliqu
School: Pittsburgh
Course: Advanced Topics In Artificial Intelligence
CS 3710 Advanced Topics in AI Lecture 3 Probabilistic graphical models Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 3710 Probabilistic graphical models Modeling uncertainty with probabilities Representing large multivariate distributions dir
School: Pittsburgh
Course: Advanced Topics In Artificial Intelligence
CS 3710 Advanced Topics in AI Lecture 6 Undirected graphical models and factors Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 3710 Probabilistic graphical models Factors Factor: is a function that maps value assignments for a subset of random
School: Pittsburgh
Course: Advanced Topics In Artificial Intelligence
Inference with Graphical Models Presented by - Amruta Purandare Date - 09/14/2005 CS3710 Advanced AI Overview Query Types Complexity of answering queries Simple Inferences for chain, loop n/ws Variable Elimination 1 Query I: Conditional Probability Given
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 13 Multiclass classification Decision trees Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Midterm exam Midterm Tuesday, March 4, 2014 In-class (75 minutes) closed book material covered
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 10 Support vector machines Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Outline Outline: Fisher Linear Discriminant Algorithms for linear decision boundary Support vector machines Max
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 9 Classification learning II Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Logistic regression model Defines a linear decision boundary Discriminant functions: g1 (x ) g (w T x ) g 0 (x)
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 15 Bayesian belief networks: Inference and learning. Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Midterm exam When: Tuesday, March 4 , 2014 Midterm is: In-class (75 minutes) closed boo
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 7 Linear regression Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Linear regression Function f : X Y is a linear combination of input components d f ( x) w0 w1 x1 w2 x 2 wd x d w0 w j x j
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 11 SVMs for regression Multilayer neural networks Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Linearly non-separable case Allow some flexibility on crossing the separating hyperplane CS
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 8 Classification learning: Logistic regression Generative classification model Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Classification Data: D cfw_d1 , d 2 ,., d n d i x i , yi
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 14 Bayesian belief networks Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Density estimation Data: D cfw_D1 , D2 ,., Dn Di x i a vector of attribute values Attributes: modeled by random
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 5 Density estimation Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Outline Outline: Density estimation: Maximum likelihood (ML) Bayesian parameter estimates MAP Bernoulli distribution
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 12 Non-parametric classification methods Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Nonparametric vs Parametric Methods Nonparametric models: More flexibility no parametric model is ne
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 2 Machine Learning Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square, x4-8845 http:/www.cs.pitt.edu/~milos/courses/cs2750/ CS 2750 Machine Learning Types of learning Supervised learning Learning mapping between inpu
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 3 Density estimation Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Announcements Homework 1: due on Thursday, January 23 before the class You should submit: A hardcopy of the report (bef
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 6 Nonparametric density estimation Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Parametric density estimation Parametric density estimation: A set of random variables X cfw_ X 1 , X 2 ,
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 4 Density estimation Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Density estimation Data: D cfw_D1 , D2 ,., Dn Di x i a vector of attribute values Objective: try to estimate the underly
School: Pittsburgh
Course: Machine Learning
CS 2750 Machine Learning Lecture 1 Machine Learning Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square, x4-8845 http:/www.cs.pitt.edu/~milos/courses/cs2750/ CS 2750 Machine Learning Administration Instructor: Milos Hauskrecht milos@cs.pitt.edu 5329 Se
School: Pittsburgh
Course: Knowledge Representation
CS 2740 Knowledge Representation Lecture 4 Propositional logic Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2740 Knowledge Representation M. Hauskrecht Administration Homework assignment 1 is out Due next week on Wednesday, September 17 Prob
School: Pittsburgh
Course: Intro To Artificl Intelligence
CS 1571 Introduction to AI Lecture 9 Methods for finding optimal configurations Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 1571 Intro to AI M. Hauskrecht Announcements Homework assignment 3 is out Due on Thursday next week ! Programming
School: Pittsburgh
Course: Intro To Artificl Intelligence
CS 1571 Introduction to AI Lecture 6 Informed search methods Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 1571 Intro to AI M. Hauskrecht Announcements Homework assignment 2 is out Due on Tuesday, September 24, 2013 before the class Two part
School: Pittsburgh
Course: Intro To Artificl Intelligence
CS 1571 Introduction to AI Lecture 5 Uninformed search methods II. Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 1571 Intro to AI M. Hauskrecht Uninformed methods Uninformed search methods use only information available in the problem definit
School: Pittsburgh
Course: Intro To Artificl Intelligence
CS 1571 Introduction to AI Lecture 4 Uninformed search methods Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 1571 Intro to AI M. Hauskrecht Announcements Homework assignment 1 Due on Tuesday, September 17, 2013 before the lecture Theoretical
School: Pittsburgh
Course: Intro To Artificl Intelligence
CS 1571 Introduction to AI Lecture 13 Propositional logic Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 1571 Intro to AI M. Hauskrecht Logical inference problem Logical inference problem: Given: a knowledge base KB (a set of sentences) and
School: Pittsburgh
Course: Intro To Artificl Intelligence
CS 1571 Introduction to AI Lecture 2 AI applications Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 1571 Intro to AI M. Hauskrecht Artificial Intelligence The field of Artificial intelligence: The design and study of computer systems that beh
School: Pittsburgh
Course: Intro To Artificl Intelligence
CS 1571 Introduction to AI Lecture 11 Knowledge Representation. Propositional logic. Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 1571 Intro to AI M. Hauskrecht Announcements Homework assignment 3 due today Homework assignment 4 is out Pro
School: Pittsburgh
Course: Intro To Artificl Intelligence
CS 1571 Introduction to AI Lecture 3 Problem solving by searching Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 1571 Intro to AI M. Hauskrecht A search problem Many interesting problems in science and engineering are solved using search A sear
School: Pittsburgh
Course: Intro To Artificl Intelligence
CS 1571 Introduction to AI Lecture 12 Propositional logic Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 1571 Intro to AI M. Hauskrecht Logical inference problem Logical inference problem: Given: a knowledge base KB (a set of sentences) and
School: Pittsburgh
Course: Intro To Artificl Intelligence
CS 1571 Introduction to AI Lecture 10 Finding optimal configurations Adversarial search Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 1571 Intro to AI M. Hauskrecht Parametric optimization Optimal configuration search: Configurations are desc
School: Pittsburgh
Course: Intro To Artificl Intelligence
CS 1571 Introduction to AI Lecture 15 Inference in first-order logic Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 1571 Intro to AI M. Hauskrecht Logical inference in FOL Logical inference problem: Given a knowledge base KB (a set of sentence
School: Pittsburgh
Course: Intro To Artificl Intelligence
CS 1571 Introduction to AI Lecture 1 Course overview Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 1571 Intro to AI M. Hauskrecht Course administrivia Instructor: Milos Hauskrecht 5329 Sennott Square milos@cs.pitt.edu TA: CharmGil Hong 5406 Se
School: Pittsburgh
Course: Intro To Artificl Intelligence
CS 1571 Introduction to AI Lecture 7 Constraint satisfaction search Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 1571 Intro to AI M. Hauskrecht Search methods Uninformed search methods Breadth-first search (BFS) Depth-first search (DFS) I
School: Pittsburgh
Course: Intro To Artificl Intelligence
CS 1571 Introduction to AI Lecture 14 First-order logic Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 1571 Intro to AI M. Hauskrecht Administration announcements Midterm: Thursday, October 31, 2013 In-class Closed book CS 1571 Intro to AI M
School: Pittsburgh
Course: Intro To Artificl Intelligence
CS 1571 Introduction to AI Lecture 8 Constraint satisfaction search. Combinatorial optimization search. Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 1571 Intro to AI M. Hauskrecht Constraint satisfaction problem (CSP) Objective: Find a confi
School: Pittsburgh
Course: Discrete Structures For Computer Science
CS 441 Discrete Mathematics for CS Lecture 10 Sequences and summations Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 441 Discrete mathematics for CS M. Hauskrecht Sequences Definition: A sequence is a function from a subset of the set of integ
School: Pittsburgh
Course: Discrete Structures For Computer Science
CS 441 Discrete Mathematics for CS Lecture 11 Countable and uncountable sets. Matrices. Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 441 Discrete mathematics for CS M. Hauskrecht Arithmetic series Definition: The sum of the terms of the arith
School: Pittsburgh
Course: Discrete Structures For Computer Science
CS 441 Discrete Mathematics for CS Lecture 7 Sets and set operations Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 441 Discrete mathematics for CS M. Hauskrecht Basic discrete structures Discrete math = study of the discrete structures used
School: Pittsburgh
Course: Discrete Structures For Computer Science
CS 441 Discrete Mathematics for CS Lecture 2 Propositional logic Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 441 Discrete mathematics for CS M. Hauskrecht Course administration Homework 1 First homework assignment is out today will be poste
School: Pittsburgh
Course: Discrete Structures For Computer Science
CS 441 Discrete Mathematics for CS Lecture 15 Mathematical induction & Recursion Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 441 Discrete mathematics for CS M. Hauskrecht Proofs Basic proof methods: Direct, Indirect, Contradiction, By Cases
School: Pittsburgh
Course: Discrete Structures For Computer Science
CS 441 Discrete Mathematics for CS Lecture 5 Predicate logic Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 441 Discrete mathematics for CS M. Hauskrecht Negation of quantifiers English statement: Nothing is perfect. Translation: x Perfect(x)
School: Pittsburgh
Course: Discrete Structures For Computer Science
CS 441 Discrete Mathematics for CS Lecture 12 Integers and division Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 441 Discrete mathematics for CS M. Hauskrecht Symmetric matrix Definition: A square matrix A is called symmetric if A = AT. Thu
School: Pittsburgh
Course: Discrete Structures For Computer Science
CS 441 Discrete Mathematics for CS Lecture 9 Functions II Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square M. Hauskrecht CS 441 Discrete mathematics for CS Functions Definition: Let A and B be two sets. A function from A to B, denoted f : A B , is
School: Pittsburgh
Course: Discrete Structures For Computer Science
CS 441 Discrete Mathematics for CS Lecture 14 Integers: applications, base conversions. Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 441 Discrete mathematics for CS M. Hauskrecht Modular arithmetic in CS Modular arithmetic and congruencies ar
School: Pittsburgh
Course: Discrete Structures For Computer Science
CS 441 Discrete Mathematics for CS Lecture 13 Integers and division Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 441 Discrete mathematics for CS M. Hauskrecht Integers and division Number theory is a branch of mathematics that explores integ
School: Pittsburgh
Course: Discrete Structures For Computer Science
CS 441 Discrete Mathematics for CS Lecture 8 Sets and set operations: cont. Functions. Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 441 Discrete mathematics for CS M. Hauskrecht Set Definition: A set is a (unordered) collection of objects. T
School: Pittsburgh
Course: Discrete Structures For Computer Science
CS 441 Discrete Mathematics for CS Lecture 3 Predicate logic Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 441 Discrete mathematics for CS M. Hauskrecht Propositional logic: review Propositional logic: a formal language for making logical inf
School: Pittsburgh
Course: Discrete Structures For Computer Science
CS 441 Discrete Mathematics for CS Lecture 4 Predicate logic Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 441 Discrete mathematics for CS M. Hauskrecht Announcements Homework assignment 1 due today Homework assignment 2: posted on the cour
School: Pittsburgh
Course: Discrete Structures For Computer Science
CS 441 Discrete Mathematics for CS Lecture 6 Informal proofs Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 441 Discrete mathematics for CS M. Hauskrecht Proofs The truth value of some statements about the world are obvious and easy to assess
School: Pittsburgh
Course: Design & Analysis Of Algorthms
CS 1510 Midterm 2 Fall 2010 1. (20 points) Consider the following two problems: TRAVELING SALESMAN INPUT: points (x1 , y1 ), . . . (xn , yn ) in the Euclidean plane OUTPUT: The shortest route, starting from the origin, that visits all the points. You can
School: Pittsburgh
Course: Design & Analysis Of Algorthms
CS 1510 Midterm 1 Fall 2011 1. (40 points) Consider the following problem. The input is a collection A = cfw_a1 , . . . , an of n points on the real line. The problem is to nd a minimum cardinality collection S of unit length intervals that cover every p
School: Pittsburgh
Course: Design & Analysis Of Algorthms
CS 1510 Midterm 2 Fall 2010 The test is a bit long. I suggest keeping your answers short and to the point. 1. (a) (5 points) Consider the standard EREW algorithm for adding n integers that runs in time T (n, p) = n/p + log p with p processors. Dene the ec
School: Pittsburgh
Course: Design & Analysis Of Algorthms
CS 1510 Midterm 1 Fall 2010 1. (40 points) We consider the following scheduling problem: INPUT: A collection of jobs J1 , . . . , Jn , where the ith job is a tuple (ri , xi ) of non-negative integers specifying the release time and size of the job. OUTPUT
School: Pittsburgh
Course: Design & Analysis Of Algorthms
CS 1510 Midterm 2 Fall 2009 The test is a bit long. I suggest keeping your answers short and to the point. 1. (a) (5 points) According to your instructor, what is the most important reason that multiplicative constants are not taken into account when comp
School: Pittsburgh
Course: Design & Analysis Of Algorthms
CS 1510 Midterm 1 Fall 2009 1. (40 points) We consider the following problem: INPUT: A collection W of positive integer weights w1 , . . . , wn. OUTPUT: A binary tree T with n leaves, where each leaf is labeled with a unique weight w() from W , that minim
School: Pittsburgh
Course: Design & Analysis Of Algorthms
CS 1510 Midterm 2 Fall 2008 1. (20 points) Consider the following two problems: MATRIX MULTIPLICATION INPUT: n by n matrices A and B OUTPUT: The product AB MATRIX SQUARING INPUT: m by m matrix C OUTPUT: The square of the matrix C Assume that Matrix Multip
School: Pittsburgh
Course: Design & Analysis Of Algorthms
CS 1510 Midterm 1 Fall 2008 1. (40 points) We consider the following problem: INPUT: A collection of jobs J1 , . . . , Jn , where the ith job is a 3-tuple (ri , xi, di) of non-negative integers. OUTPUT: 1 if there is a preemptive feasible schedule for the
School: Pittsburgh
Course: Design & Analysis Of Algorthms
CS 1510 Midterm 1 Fall 2007 1. (40 points) You wish to drive from point A to point B along a highway minimizing the time that you are stopped for gas. You are told beforehand the capacity C of you gas tank in liters, your rate F of fuel consumption in lit
School: Pittsburgh
Course: Intro To CS
/* * FILE : testLinkedList.c * AUTHOR : Jeffrey Hunter * WEB : http:/www.iDevelopment.info * NOTES : Example program that tests the * Linked List implementation. */ #include <stdio.h> #include "list.h" void PrintList(const List L) cfw_ Position P =
School: Pittsburgh
Course: Intermediate Programming
import java.util.*; import java.io.*; public class Final cfw_ public static void main(String[] args) cfw_ if(args.length < 1) cfw_ System.out.printf("Enter name of file on command line!"); System.exit(0); try cfw_ File f = new File(args[0]); Scanner infi
School: Pittsburgh
Course: INTRODUCTION TO COMPUTER ARCHITECTURE
Question 1 (5+5=10 points) (a) The maximum number of Format 1 instructions that can be supported by the ISA is _63_ and the maximum number of Format 2 instructions that can be supported by the ISA is _16_. (b) The maximum is determined by the range of the
School: Pittsburgh
Course: Introduction To Algorithms
Final Computer Science 2150 Introduction to Algorithms Spring 2008 Instructions: 1. The test is closed book, closed notes. 2. If you are taking the nal for prelimary exam credit, then write your prelimary number given to you by Keena on the exam answer sh
School: Pittsburgh
Course: Introduction To Algorithms
Final Computer Science 2150 Introduction to Algorithms Spring 2009 Instructions: 1. The test is closed book, closed notes. 2. If you are taking the nal for preliminary exam credit, then write your preliminary number given to you by Keena on the exam answe
School: Pittsburgh
Course: Introduction To Algorithms
Final Computer Science 2150 Introduction to Algorithms Spring 2010 Instructions: 1. The test is closed book, closed notes. 2. If you are taking the nal for preliminary exam credit, then write your preliminary number given to you by Keena on the exam answe
School: Pittsburgh
Course: Introduction To Algorithms
Final Computer Science 2150 Introduction to Algorithms Spring 2011 Instructions: 1. The test is closed book, closed notes. 2. For most of the problems, I am interested in testing whether you understand the techniques and concepts more than I am interested
School: Pittsburgh
Course: Introduction To Algorithms
Final Computer Science 2150 Introduction to Algorithms Spring 2012 Instructions: 1. The test is closed book, closed notes. 2. For most of the problems, I am interested in testing whether you understand the techniques and concepts more than I am interested
School: Pittsburgh
Course: Web Site Design And Development
Final Pratice Exam 1 Whichofthefollowingprovidesanerrorcheckingmechanismthattakescareof packetsthatarelostordelayedintransit:A a. a. TransmissionControlProtocol(TCP) b. InternetProtocol(IP) c. HypertextTransferProtocol(HTTP) d. InternetExplorerCheckingPr
School: Pittsburgh
Course: Web Site Design And Development
Midterm Example 1. Which of the following provides an error-checking mechanism that takes care of packets that are lost or delayed in transit: a. Transmission control protocol (TCP) b. Internet protocol (IP) c. Hypertext transfer protocol (HTTP) d. Intern
School: Pittsburgh
Course: Introduction To Artificial Intelligence
To choose a variable: Choose the variable with the minimum remaining values ("MRV" heuristic) most constrained we'll find out sooner if the current assignment is doomed to fail Tie, choose the variable that is involved in the most constraints with
School: Pittsburgh
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CS1501 Fall 2008 Test-3 Instructions: Complete your work on this test booklet and put your nal answers on the answer sheet. Put your name and email on both the test booklet and the answer sheet and hand them to the instructor. Only the answer sheet will b
School: Pittsburgh
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CS1501 Fall 2008 Test-2 Instructions: Complete your work on this test booklet and put your nal answers on the answer sheet. Put your name and email on both the test booklet and the answer sheet and hand them to the instructor. Only the answer sheet will b
School: Pittsburgh
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School: Pittsburgh
CS1501 Fall 2008 Test-1 Instructions: Complete your work on this test booklet and put your nal answers on the answer sheet. Put your name and email on both the test booklet and the answer sheet and hand them to the instructor. Only the answer sheet will b
School: Pittsburgh
= CS1501 Test 3 = Problem 1, Optimal BST 1-7, 10 points D / \ B E / \ A C = Problem 2, Digital Search Tree 1-8, 10 points A / \ B R / S = Problem 3, Radix Search Trie 1-9, 10 points * / \ * * / / * * / / * * / \ \ A B * / \ R S = Prob
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- CS1501 Test 2 - Problem 1, Patricia, 10 points You must do a full-key comparison at the end. - Problem 2, Topological Sort, 15 points A. 6 B. 1 C. c e b d a -or- a d b e c - Problem 3, Prim's Algorithm, 15 points A. 3 B. 11 C. 17 - Problem 4, Maxi
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- CS1501 Test-1 - Problem 1, Complexity, 20 points A. O(n^2) B. O(n log n) C. O(n^2) D. O(2^n) - Problem 2, AVL Tree, 20 points 100 / \ 10 200 / \ / \ 5 50 150 300 - Problem 3, Radix Search Trie, 20 points * / \ * * / / * * / / * * / \ \ A
School: Pittsburgh
Course: Intro To Operating Systems
Test Questions Chapter 2 Modern Operating Systems, 2nd ed. Multiple choice in the blank at the left place the letter of the word or phrase which best answers the question or completes the statement. 1. _ What concept allows multiple executions to
School: Pittsburgh
Course: Algorithm Implementation
+ + + + + + + + + Solution Found: a b a b a d a d a Solution Found: a b a b a d a m a Solution Found: a b a b a d a n a Solution Found: a b a b a d e g o Solution Found: a b a
School: Pittsburgh
Course: Software Engineering
Midterm Exam CS1530 October 18, 2004 Sample Solution 45 points total. 1. (5 points) Name and briefly describe the five perspectives on quality according to Garvin. Transcendental view: where quality is something we can recognize but not define User v
School: Pittsburgh
CS134 Web Site Design & Development Midterm Exam Name: _ Email: _ Score: _ (Part One) I True or false questions (T for true, F for false; 2 points each, total 8 points) 1. Not every document/resource on the WWW needs to have an identifier in order t
School: Pittsburgh
public class ArrayTalk{ public static void main(String[] args){ int[][] ar = {1,2,3,4}, {5,6,7,8}, {9,10,11,12}; System.out.println("rows = " + ar.length); System.out.println("cols = " + ar[0].length); System.out.println(ar[1][2]); int[][] br = {1,2}
School: Pittsburgh
import java.util.Vector; public class UseVector{ public static void main(String[]args){ Vector<String> vec = new Vector<String>(); /create an empty Vector of String vec.add("Java"); /add element to rear of vector vec.add("Bob"); vec.add("Ann"); vec.a
School: Pittsburgh
Course: Introduction To Computer Graphics
cs1566 Homework 2 (part 1) Stitcher Algo CS1566 Assignment 2 (part 1) Solutions Stitcher Algorithm Problem 1 Your boss at Acme Graphics Corp. wants you to write a procedure to first compute and then render a sphere in OpenGL. His evil plan is to mutilate
School: Pittsburgh
Course: Knowledge Representation
University of Pittsburgh CS 2740 Knowledge Representation Professor Milos Hauskrecht Handout 15 December 5, 2008 Problem set 6 solutions Uncertainty Problem 1 Let A,B,C be random variables. A and B have two possible values T,F and C has three values: high
School: Pittsburgh
Course: Knowledge Representation
University of Pittsburgh CS 2740 Knowledge Representation Professor Milos Hauskrecht Handout 9 October 15, 2008 Solutions to problem set 4 Problem 1. Unication See example solution on the course web site. Problem 2. Inference in FOL Let L be a rst-order l
School: Pittsburgh
Course: Knowledge Representation
University of Pittsburgh CS 2740 Knowledge Representation Professor Milos Hauskrecht Handout 14 December 5, 2008 Problem set 5 solutions STRIPS Planning Problem 1 Consider a simple blocks world problem: C B A C Initial state D Goal state B A D We use pred
School: Pittsburgh
Course: Knowledge Representation
University of Pittsburgh CS 2740 Knowledge Representation Professor Milos Hauskrecht Handout 16 December 5, 2008 Problem set 7 solutions Uncertainty. Problem 1. Bayesian Belief Networks. Assume the Bayesian belief network for the diagnosis of cars electir
School: Pittsburgh
Course: Knowledge Representation
University of Pittsburgh CS 2740 Knowledge Representation Professor Milos Hauskrecht Handout 5 October 1, 2008 Problem assignment 4 Due: Wednesday, October 8, 2008 Unication The unication process in FOL aims to nd the most general substitution that makes
School: Pittsburgh
Course: Knowledge Representation
University of Pittsburgh CS 2740 Knowledge Representation Professor Milos Hauskrecht Handout 8 October 8, 2008 Solutions to problem set 3 Problem 1. Inference with propositional rules. Assume a simplied animal identication problem due to P. Winston. The k
School: Pittsburgh
Course: Knowledge Representation
University of Pittsburgh CS 2740 Knowledge Representation Professor Milos Hauskrecht Handout 17 December 5, 2008 Problem set 8 solutions Uncertainty. Problem 1. Monte Carlo sampling Assume the Bayesian belief network for the diagnosis of car's electrical
School: Pittsburgh
Course: Knowledge Representation
University of Pittsburgh CS 2740 Knowledge Representation Professor Milos Hauskrecht Handout 6 October 8, 2008 Solutions to problem set 1 Problem 1 Thank you. Problem 2 and 3 Please see solutions on the course web page. Problem 4. Translation Translate th
School: Pittsburgh
Course: Knowledge Representation
University of Pittsburgh CS 2740 Knowledge Representation Professor Milos Hauskrecht Handout 7 October 8, 2008 Solutions to problem set 2 Problem 1. Equivalences Determine whether the two expressions are logically equivalent, that is, for each interpretat
School: Pittsburgh
Course: Knowledge Representation
University of Pittsburgh CS 2740 Knowledge Representation Professor Milos Hauskrecht Handout 3 September 17, 2008 Problem assignment 2 Due: Wednesday, September 24, 2000 Propositional logic Problem 1 Determine whether the two expressions are logically equ
School: Pittsburgh
Course: Knowledge Representation
University of Pittsburgh CS 2740 Knowledge Representation Professor Milos Hauskrecht Handout 4 September 24, 2008 Problem assignment 3 Due: Wednesday, October 1 2008 Problem 1. Inference with propositional rules. Assume a simplied animal identication prob
School: Pittsburgh
Course: Knowledge Representation
University of Pittsburgh CS 2740 Knowledge Representation Professor Milos Hauskrecht Handout 12 November 6, 2008 Problem assignment 6 Due: Wednesday, November 12, 2008 Probability theory Problem 1 Let A,B,C be random variables. A and B have two possible v
School: Pittsburgh
Course: Knowledge Representation
University of Pittsburgh CS 2740 Knowledge Representation Professor Milos Hauskrecht Handout 12 October 22, 2008 Problem assignment 5 Due: Wednesday, October 29, 2008 Planning Problem 1 Consider a simple blocks world problem: C B A C Initial state D Goal
School: Pittsburgh
Course: Knowledge Representation
University of Pittsburgh CS 2740 Knowledge Representation Professor Milos Hauskrecht Handout 2 September 10, 2008 Problem assignment 1 Due: Wednesday, September 17, 2008 Lisp The goal of this assignment is to practice your lisp programming skills on two s
School: Pittsburgh
Course: Design & Analysis Of Algorthms
CS 1510 Adversarial Lower Bound Arguments 1. In a simplied form of the game mastermind there is a hidden sequence H = (c1 , . . . , ck ) of k colored pegs. There are C dierent possible colors. Colors can be repeated in the hidden sequence. The game consis
School: Pittsburgh
Course: Design & Analysis Of Algorthms
CS 1510 Approximiation Algorithms Problems 1. Consider the vertex cover problem, that is, given a graph G nd a minimal cardinality collection S of vertices with the property that every edge in G is incident to a vertex in S. Consider the following algorit
School: Pittsburgh
Course: Design & Analysis Of Algorthms
CS 1510 Dynamic Programming Homework Problems 1. (2 points) Consider the recurrence relation T (0) = T (1) = 2 and for n > 1 n1 T (i)T (i 1) T (n) = i=1 We consider the problem of computing T (n) from n. (a) Show that if you implement this recursion direc
School: Pittsburgh
Course: Design & Analysis Of Algorthms
CS 1510 Greedy Homework Problems 1. (2 points) Consider the following problem: INPUT: A set S = cfw_(xi , yi )|1 i n of intervals over the real line. OUTPUT: A maximum cardinality subset S of S such that no pair of intervals in S overlap. Consider the fol
School: Pittsburgh
Course: Design & Analysis Of Algorthms
CS 1510 Parallel Algorithms Homework Problems 1. (2 Points) Consider the problem of computing the AND of n bits. Give an algorithm that runs in time O(log n) using n processors on an EREW PRAM. What is the eciency of this algorithm? Using the folding p
School: Pittsburgh
Course: Design & Analysis Of Algorthms
CS 1510 Reductions and NP-hardness Homework Problems 1. (2 points) A square matrix M is lower triangular if each entry above the main diagonal is zero, that is, each entry Mi,j , with i < j, is equal to zero. Show that if there is an O(n2 ) time algorithm
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Course: Intro To CS
/* File: cmdlineargs.c */ #include <stdio.h> #include <stdlib.h> #include <string.h> int checkint(char *); void reverse(char *); int main(int argc, char *argv[]) cfw_ int sum = 0; /* Initialize sum of integers in command line */ while (-argc) cfw_ /* Loo
School: Pittsburgh
Course: Intro To CS
XML - Application Programming Interfaces (APIs) Prepared by Jeff Hunter, Sr. DBA 12-OCT-2002 Overview The most important decision you'll make at the start of an XML project is the applicationprogramming interface (API) you'll use. Many APIs are implemente
School: Pittsburgh
Course: Intro To CS
Understanding Dynamic Shared Object (DSO) Support by Jeff Hunter, Sr. Database Administrator Dynamic Shared Object (DSO) Support The Apache HTTP Server is a modular program where the administrator can choose the functionality to include in the server by s
School: Pittsburgh
Course: Intro To CS
/ - / TreeSetExample.java / - /* * = * Copyright (c) 1998-2011 Jeffrey M. Hunter. All rights reserved. * * All source code and material located at the Internet address of * http:/www.idevelopment.info is the copyright of Jeffrey M. Hunter and * is
School: Pittsburgh
Course: Intro To CS
/ - / SimpleCalc1.java / - /* * = * Copyright (c) 1998-2011 Jeffrey M. Hunter. All rights reserved. * * All source code and material located at the Internet address of * http:/www.idevelopment.info is the copyright of Jeffrey M. Hunter, 2005 and *
School: Pittsburgh
Course: Intro To CS
J2EE / Web Programming BEA WebLogic - (Release 8.1.2) Installing BEA WebLogic Starting / Stopping WebLogic Server Running BEA WebLogic Examples Java and Web Technologies in Oracle JDBC Examples De-support Information - (J2EE & Corba in the Oracle9i Databa
School: Pittsburgh
Course: Intro To CS
Software Change / Configuration Management Concurrent Versions System - (UNIX / Release 1.11.14) Installing CVS (UNIX - Release 1.11.14) Creating a CVS Repository Logging into CVS / Setting CVSROOT Importing Projects into CVS CVS Commands Removing Project
School: Pittsburgh
Course: Intro To CS
/ - / Predicate.java / - /* * = * Copyright (c) 1998-2011 Jeffrey M. Hunter. All rights reserved. * * All source code and material located at the Internet address of * http:/www.idevelopment.info is the copyright of Jeffrey M. Hunter and * is prote
School: Pittsburgh
Course: Intro To CS
/ - / PredicateTest.java / - /* * = * Copyright (c) 1998-2011 Jeffrey M. Hunter. All rights reserved. * * All source code and material located at the Internet address of * http:/www.idevelopment.info is the copyright of Jeffrey M. Hunter and * is p
School: Pittsburgh
Course: Intro To CS
/ - / ObjectEquality.java / - /* * = * Copyright (c) 1998-2011 Jeffrey M. Hunter. All rights reserved. * * All source code and material located at the Internet address of * http:/www.idevelopment.info is the copyright of Jeffrey M. Hunter and * is
School: Pittsburgh
Course: Intro To CS
Object-Oriented Techniques by Jeff Hunter, Sr. Database Administrator Introduction The Java API was designed and built on the OO model. Having a good understanding of Design Patterns such as Factory, Bridge and Delegate, though not required to be a Java p
School: Pittsburgh
Course: Intro To CS
/ - / LinkedListExample.java / - /* * = * Copyright (c) 1998-2011 Jeffrey M. Hunter. All rights reserved. * * All source code and material located at the Internet address of * http:/www.idevelopment.info is the copyright of Jeffrey M. Hunter and *
School: Pittsburgh
Course: Intro To CS
Description of the JavaCC Grammar File This web page contains the complete syntax of Java Compiler Compiler grammar files with detailed explanations of each construct. Tokens in the grammar files follow the same conventions as for Java. Hence identifiers,
School: Pittsburgh
Course: Intro To CS
Document Type Definition (DTD) Objectives To understand what a DTD is To be able to write DTDs To be able to declare elements and attributes in a DTD To understand the difference between general entities and parameter entities To be able to use conditiona
School: Pittsburgh
Course: Intro To CS
Installing / Configuring the Apache Web Server (Release 2.0.43) by Jeff Hunter, Sr. Database Administrator Overview The Apache Web Server source code can be downloaded from the Apache Web Site. The software is located at: http:/httpd.apache.org/download.c
School: Pittsburgh
Course: Intro To CS
/ - / HashCodeExample.java / - /* * = * Copyright (c) 1998-2011 Jeffrey M. Hunter. All rights reserved. * * All source code and material located at the Internet address of * http:/www.idevelopment.info is the copyright of Jeffrey M. Hunter and * is
School: Pittsburgh
Course: Intro To CS
/ - / DOMExample.java / - /* * = * Copyright (c) 1998-2011 Jeffrey M. Hunter. All rights reserved. * * All source code and material located at the Internet address of * http:/www.idevelopment.info is the copyright of Jeffrey M. Hunter and * is prot
School: Pittsburgh
Course: Intro To CS
Collections in Java Arrays n Has special language support Iterators n Iterator (i) Collections (also called containers) n n Collection (i) Set (i), u n List (i), u n HashSet (c), TreeSet (c) ArrayList (c), LinkedList (c) Map (i), u OOP: Collections Has
School: Pittsburgh
Course: Intro To CS
/ - / CrimsonXmlDomExample.java / - /* * = * Copyright (c) 1998-2011 Jeffrey M. Hunter. All rights reserved. * * All source code and material located at the Internet address of * http:/www.idevelopment.info is the copyright of Jeffrey M. Hunter and
School: Pittsburgh
Course: Intro To CS
Java Collections Framework Overview Return to the Java Programming Corner. Contents Introduction Java 2 Collections Framework Overview The Collection Interfaces Java 2 Collections Libraries Arrays Iterator The Collection Interface The Set Interface The Li
School: Pittsburgh
Course: Intro To CS
/ - / CloningExample.java / - /* * = * Copyright (c) 1998-2011 Jeffrey M. Hunter. All rights reserved. * * All source code and material located at the Internet address of * http:/www.idevelopment.info is the copyright of Jeffrey M. Hunter and * is
School: Pittsburgh
Course: Intro To CS
. Advanced search IBM home | Products & services | Support & downloads | My account IBM developerWorks : XML zone : XML zone articles All about JAXP Sun's Java API for XML parsing Brett McLaughlin (brett@newInstance.com) Enhydra Strategist, Lutris Technol
School: Pittsburgh
Course: Intro To CS
/ - / ArrayExample.java / - /* * = * Copyright (c) 1998-2011 Jeffrey M. Hunter. All rights reserved. * * All source code and material located at the Internet address of * http:/www.idevelopment.info is the copyright of Jeffrey M. Hunter and * is pr
School: Pittsburgh
Course: Intro To CS
/ - / ClassExample.java / - /* * = * Copyright (c) 1998-2011 Jeffrey M. Hunter. All rights reserved. * * All source code and material located at the Internet address of * http:/www.idevelopment.info is the copyright of Jeffrey M. Hunter and * is pr
School: Pittsburgh
Course: Intro To CS
/ - / CalendarExample.java / - /* * = * Copyright (c) 1998-2011 Jeffrey M. Hunter. All rights reserved. * * All source code and material located at the Internet address of * http:/www.idevelopment.info is the copyright of Jeffrey M. Hunter and * is
School: Pittsburgh
Course: Introduction To Computer Graphics
cs1566 Homework 3 (part 1) Transformer Algo CS1566 Assignment 3 (part 1) Solutions Transformer Algorithm Grading: Problem 1 2 3 4 5 6 7 8 Points 3 2 2 2 1 1 15 4 1. For Assignment 3, we ask you to move in space, along w
School: Pittsburgh
Course: Introduction To Computer Graphics
cs1566 Fall 2012 Homework 4 (part 1) Camera Modeler Algo CS1566 Assignment 4 (part 1) Camera Modeler Algorithm Out: Thu 10/11 Due: Tue 10/16 5:00pm Grading: Problem 1 2 3 4 5 6 7 Total Points 15 15 15 15 15 15 10 100 1. Consider an orthographic (parallel)
School: Pittsburgh
Course: Introduction To Computer Graphics
cs1566 Fall 2012 Homework 5 (part 1) Intersect Algo CS1566 Assignment 5 (part 1) Intersect Algorithm Out: Fri 10/26 Due: Wed 10/31 5:00pm Grading: Problem 1 2 3 4 5 6 7 8 9 10 Total Points 10 10 10 10 10 10 10 10 10 10 100 In class, we have worked with Pw
School: Pittsburgh
Course: Discrete Mathematics
Homework 1 Solutions University of Pittsburgh Computer Science Department CS441 Discrete Structures for Computer Science Instructor: Milos Hauskrecht Problems from Chapter 1.1. Problem 2 a) Not a proposition b) Not a proposition c) False proposition d) No
School: Pittsburgh
Course: Discrete Mathematics
Homework 2 Solutions University of Pittsburgh Computer Science Department CS441 Discrete Structures for Computer Science Instructor: Milos Hauskrecht Section 1.2 solutions 6. p T T F F q T F T F pq T F F F (pq) F T T T q p F T T T 8. a.) (p q) <=> p q Kwa
School: Pittsburgh
Course: Discrete Mathematics
Homework 3 Solutions University of Pittsburgh Computer Science Department CS441 Discrete Structures for Computer Science Instructor: Milos Hauskrecht 2. Section 1.4 12. a) b) c) d) e) f) g) True True False True False True False 16. a) True (square root of
School: Pittsburgh
Course: Discrete Mathematics
Homework 4 Solutions University of Pittsburgh Computer Science Department CS441 Discrete Structures for Computer Science Instructor: Milos Hauskrecht Problem 2 (Section 2.1) 6. Each of the set is a subset of itself. Also, B is a subset of A and C is a sub
School: Pittsburgh
Course: Discrete Mathematics
Homework 5 Solutions University of Pittsburgh Computer Science Department CS441 Discrete Structures for Computer Science Instructor: Milos Hauskrecht Problem 1 2. 1). 281 = 128 2). 7 3). 1 + (1)8 = 2 4). (2)8 = 256 4. 1). a1 = 1, a2 = 2, a3 = 4, a4 = 8. 2
School: Pittsburgh
Course: Discrete Mathematics
Homework 6 Solutions University of Pittsburgh Computer Science Department CS441 Discrete Structures for Computer Science Instructor: Milos Hauskrecht Problem 1 4. Suppose a|b, so that b=at for some t, and b|c so that c = bs for some s. Then substituting t
School: Pittsburgh
Course: Discrete Mathematics
Homework 7 Solutions University of Pittsburgh Computer Science Department CS441 Discrete Structures for Computer Science Instructor: Milos Hauskrecht Chapter 5.1 4. Let P(n) be the proposition 13 + 23 + + n3 = (n(n + 1)/2)2 whenever n is a positive intege
School: Pittsburgh
Course: Discrete Mathematics
Homework 8 Solutions University of Pittsburgh Computer Science Department CS441 Discrete Structures for Computer Science Instructor: Milos Hauskrecht Problem 1 2. By the application of Theorem 2 (generalized pigeonhole principle), at least one letter will
School: Pittsburgh
Course: Discrete Mathematics
Homework 9 Solutions University of Pittsburgh Computer Science Department CS441 Discrete Structures for Computer Science Instructor: Milos Hauskrecht Problems from Chapter 7.1, 7.2, 7.3, 7.4 (7th Edition) Chapter 7.1 2. 1/6 4. 30/366 = 0.082 8. Since the
School: Pittsburgh
Course: Discrete Mathematics
Homework 10 Solutions University of Pittsburgh Computer Science Department CS441 Discrete Structures for Computer Science Instructor: Milos Hauskrecht Problem 1: Section 9.1 1. a. cfw_(0, 0), (1, 1), (2, 2), (3, 3) b. cfw_(1, 3), (2, 2), (3, 1), (4, 0) c.
School: Pittsburgh
Course: Intro To CS
3: C 3: C , C, Dev-C+, Windows gcc C Unix. , WinSCP Windows ( )
School: Pittsburgh
Course: Intro To CS
4: , 4: , , C, (for, while, do.while), (if.else) , printf(). 1: 1.1. C 100 i i =1 .
School: Pittsburgh
Course: Intro To CS
2: Unix Tutorial 2: Unix Tutorial Unix C ( gcc). , . , . /. ,
School: Pittsburgh
Course: Intermediate Programming
import java.awt.*; import java.awt.event.*; import javax.swing.*; public class Lab9 implements MouseListener, MouseMotionListener multiple interfaces cfw_ JFrame window; Container content; int mouseX,mouseY,oldX,oldY; JLabel coords; / NOTE JButton colorBu
School: Pittsburgh
Course: Intermediate Programming
/package lab8; /* * @author cinwell * @data Mar 14, 2012 * @version 1.1.0 * * @date Oct31, 2011 * @version 1.0.0 * * The Adder class displays a JFrame that lets user calculate the sum of two integers. * When the result button is clicked, the result will b
School: Pittsburgh
Course: Intermediate Programming
/ / your name: / your PittID: (i.e Joe75) import java.io.*; / I/O import java.util.*; / Scanner class public class Lab1 cfw_ public static void main( String args[] ) cfw_ Scanner kbd = new Scanner (System.in); / declare needed variables String firstName,
School: Pittsburgh
Course: Intrmedt Progrmming Using Java
CS0401 COE0401 Intermediate Java Programming Lab 9 Wednesday July 11, 2007 1. Design and implement a class called MonetaryCoin that is derived from the Coin class discussed in chapter 5. Store a value in the monetary coin that represents its value a
School: Pittsburgh
Course: Intrmedt Progrmming Using Java
CS0401 COE0401 Intermediate Java Programming Lab 4 Wednesday June 06, 2007 Exercise 1: Design and implement class Dog that contains instance data that represents the dog's name and age. Define the Dog constructor to accept and initialize instance da
School: Pittsburgh
Course: Intrmedt Progrmming Using Java
CS0401 COE0401 Intermediate Java Programming Lab 7 Wednesday June 27, 2007 A Shopping Cart In this exercise you will complete a class that implements a shopping cart as an array of items. The file Item.java contains the definition of a class named