| Date
|
Topic
|
Assignments
|
|
Aug. 25
| Linear Algebra Refresher
| Handout
|
|
Aug. 27
| Statistics Refresher
| Lecture Notes
|
|
Sept. 3
| Issues in Neural Networks and Statistical Learning
| Bishop, Ch.1, Bias-Variance Tradeoff Derivation
|
|
Sept. 8
| Introduction to Graphical Models
| Bishop, Ch.8.1 and 8.2
|
|
Sept. 10
| Supervised Learning: Linear Models for Regression
| Bishop, Ch.3
|
|
Sept. 15
| Supervised Learning: Kernel Methods for Regression
| Bishop, Ch.6
|
|
Sept. 22
| Supervised Learning: Gaussian Process Regression
| Bishop, Ch.6
|
|
Sept. 24
| The Bayesian approach to statistical learning
| Bishop, Ch.2.3.6 and 3.4
|
|
Oct. 1
| Supervised Learning: Linear classification -- Part 1
| Bishop, Ch.4
|
|
Oct. 6
|
| Homework 1
|
|
Oct. 8
| The EM Algorithm and Variational Bayes -- Part 1,
Jo-Anne's slides (pdf),
Jo-Anne's slides (ppt, with animations)
| Bishop, Ch.9.4 and 10.1
|
|
Oct. 13
| The EM Algorithm and Variational Bayes -- Part 2
| Bishop, Ch.9.4 and 10.1
|
|
Oct. 15
| Supervised Learning: Linear classification -- Part 2
| Bishop, Ch.4
|
|
Oct. 20
| Unsupervised Learning: Clustering and Mixture Models
| Bishop, Ch.9
|
|
Oct. 22
| Mixture of Experts
| Bishop, Ch.14.5
|
|
Oct. 27
| Nonparametric Methods for Density Estimation and Classification
| Bishop, Ch.2.5 and 2.6, Handout
|
|
Oct. 29
| Nonparametric Methods for Regression
| Bishop, Ch.2.5, 2.6, 6.3.1 Handout
|
|
Nov. 3
| Dimensionality Reduction - Part1
| Bishop, Ch.12, Homework 2
|
|
Nov. 5
| Dimensionality Reduction - Part2
| Bishop, Ch.12
|
|
Nov. 10
| Independent Component Analysis
| Bishop, Ch.12, Handout
|
|
Nov. 12
| Support Vector Machines
| Bishop, Ch.7
|
|
Nov. 17
| Bayesian Complexity Control and Sparsity
| Bishop, Ch.7
|
|
Nov. 19
| Associative Memory, Hopfield Nets, Boltzmann Machines
| Handout,Homework 3
|
|
Nov. 24
| Helmholtz Machines
| Handout1& Handout2
|
|
Nov. 26
| Classical Neural Network Algorithms
| Homework 4
|
|
Dec. 1
| Project Presentations (allow some extra time)
|
|
|
Dec. 3
| Final Quiz
|
|
