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Teaching » Syllabus: Neural Computation with Artificial Neural Networks

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Date

Topic

Assignments

Aug. 22

Linear Algebra Refresher Handout

Aug. 24

Statistics Refresher Lecture Notes

Aug. 29

Issues in Neural Networks Bishop, Ch.1

Aug. 31

Supervised Learning: Single Layer Classification Bishop, Ch.3

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)

Page last modified on September 01, 2006, at 12:18 PM