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, BiasVariance 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,
JoAnne's slides (pdf),
JoAnne'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

