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Teaching » Syllabus: Neural Computation with Artificial Neural Networks

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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

Outdated Course Handouts based on Bishop 1995 book: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 


Oct. 10

The EM Algorithm for Supervised Learning (Mixture of Experts)


Homework 2


The Theory of the EM Algorithm

Handout will be sent by email

Oct. 17

Nonparametric Density Estimation and Classification Handout

Oct. 19

Nonparametric Regression


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



Homework 4



Nov. 28

Review of class & 

Project Presentations

Common Project

Nov. 30

Project Presentations Common Project

Dec.9 (Friday)

Short Final Exam (closed book)


Designed by: Nerses Ohanyan & Jan Peters
Page last modified on November 28, 2008, at 04:14 PM