Computer Science Department

University of Western Ontario

CS 434s/541a Pattern Recognition

Fall 2004


Course Outline

Useful Links

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Final Project

Final Grades



Schedule
DateLecture NotesSuggested ReadingHW and Solutions
September 13 Lecture 1: Introduction and Review of Probability and StatisticsDHS Chapter 1
September 15Lecture 2: Review of Linear Algebra and Introduction to MatlabAny matlab tutorial, for example this tutorial, or matlab primer
September 20 Lecture 3: Bayesian Decision TheoryDHS Sections 2.1, 2.2, 2.3 (but not sections with stars), 2.4 Assignment 1 Due Oct.4
September 22 Lecture 3: Bayesian Decision Theory continued Note Assignment 1 has been corrected.
September 27 Lecture 4: Discriminant Functions for Gaussian Random Variable Ch 2.5 and 2.6
September 29 Lecture 5: Maximum Likelihood and Baysian Parameter Estimation Ch 3.1, 3.2, 3.3, 3.5 Note Assignment 1, Problem 1 has been corrected
October 4 Lecture 6: Nonparametric Density Estimation Ch 4.1, 4.2, 4.3 (except 4.3.1 and 4.3.2, 4.3.5), 4.4, 4.5 (except 4.5.1, 4.5.2), 4.6 (except 4.6.2) Assignment 2 , due Oct. 18. Data for this assignment
October 6 Lecture 6 continued Solutions to Assignment1 Matlab Code Problem 1 Problem 2 Problem 4
October 11 Thanskgiving
October 13 Lecture 6 continued Hints for Assignment 1, problem 1.
October 18 Lecture 7: The Curse of Dimensionality and PCACh. 3.7 and 3.8.1
October 20 Lecture 8: Fisher Linear Discriminant 3.8.2 Assignment 3 , due Nov. 10 Data and functions for this assignment
October 25 Lecture 9: Linear Discriminant Functions 5.1, 5.2, 5.4, 5.5, 5.7
October 27 Continue Lecture 9 Solution to Assignement2 and Matlab Code
Nov. 1 Lecture 10: Continuie Liner Discriminant Functions 5.8 Assignment 3 problem 2 has been modified.
Nov. 3 Review before the midterm Assignment 3 problem 1(e) and (f) has been clarified
Nov. 8 Midterm
Nov. 10 Lecture 11: Support Vector Machines 5.11 Midterm solutions
Nov. 15 Lecture 11: Continue Support Vector Machines
Nov. 17 Lecture 12: MultiLayer Neural Networks 6.1, 6.2 Assignment 4 Due Dec. 1
Nov. 22 Lecture 13: Continue MultiLayer Neural Networks 6.3 Assignment 4 problem weights adjusted
Nov. 24 Lecture 14: Continue MultiLayer Neural Networks 6.3.3, 6.5, 6.8
Nov. 30 Lecture 15: Unsupervised Learning: Clustering 10.1, 10.6, 10.7, 10.8, 10.9
Dec. 1 Lecture 16: Finish Clustering example in slide 27 corrected, example in slide 19 actually works
Dec. 6 Lecture 17: EM algorithm and Parametric Unsupervised Clustering
Dec. 8 Lecture 18: Low dimensional Representation of High dimensional Data Assignment 4 solutions (short)