| 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) |
|
|---|