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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


Outdated Course Handouts based on Bishop 1995 book:
Sept. 7
 Supervised Learning: Single Layer Regression
 Bishop, Ch.3

Sept. 12
 Supervised Learning: Multiple Layer Networks
 Bishop, Ch.4

Sept. 14
 Supervised Learning: Spatially Localized Systems
 Bishop, Ch.5

Sept. 19
 Forward & Inverse Models, Distal Teachers

Handout
Homework 1

Sept. 21
 Unsupervised Learning: PCA and Autoencoders
 Handout

Sept. 26

Unsupervised Learning: Cluster Analysis and Density Estimation
 Bishop, Ch.2

Sept. 28
 Learning and Generalization
 Bishop, Ch.9

Oct. 3
 Maximum Likelihood & Error Functions
 Bishop, Ch.6

Oct. 5
 The EM Algorithm for Density Estimation (Mixture Models)
 Bishop, Ch.2
Handout

Oct. 10

The EM Algorithm for Supervised Learning (Mixture of Experts)
 Handout,
Homework 2

Oct.12

The Theory of the EM Algorithm
 Handout will be sent by email

Oct. 17
 Nonparametric Density Estimation and Classification
 Handout

Oct. 19

Nonparametric Regression
 Handout

Oct. 24

Data Preprocessing and Dimensionality Reduction

Bishop, Ch.8

Oct. 26
 Bayesian Neural Networks
 Bishop, Ch.10

Oct. 31
 Information Theory: Minimum Description Length, Entropy, and Mutual Information
 Bishop 6.10 & 10.10

Nov. 2
 Hopefield Networks and Boltzmann Machines
 Handout Homework 3

Nov. 7
 Helmholtz Machines
 Handout1& Handout2

Nov. 9
 Independent Component Analysis
 Handout

Nov. 14
 Reinforcement Learning: Introduction and Dynamic Programming
 Electronic Book

Nov. 16
 Reinforcement Learning: >Actor Critic Systems
 Electronic Book

Nov.21
 TBA

Homework 4

Nov.23
 TBA


Nov. 28
 Review of class &
Project Presentations

Common Project

Nov. 30
 Project Presentations
 Common Project

Dec.9 (Friday)
 Short Final Exam (closed book)
:)


