Date  Lecture Notes  Suggested Reading  HW and Solutions


September 13  Lecture 1: Introduction and Review of Probability and Statistics  DHS Chapter 1 


September 15  Lecture 2: Review of Linear Algebra and Introduction to Matlab  Any matlab tutorial, for example this tutorial, or matlab primer 


September 20  Lecture 3: Bayesian Decision Theory  DHS 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 PCA  Ch. 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) 

