Week (Date) |
Topic |
|
|
---|---|---|---|
Week 1 (Sep 13) |
Introduction to Neural Networks The Perceptron |
|
Lecture 1 |
Week 2 (Sep 20) |
Learning Paradigms, LMS Algorithm Lab Project 1: Single-layer Perceptron |
2 3 |
Lecture 2 |
Week 3 (Sep 27) |
Multi-layer Perceptrons (MLP) Backpropagation Algorithm Lab Project 2: Multi-layer Perceptron |
4 |
Lecture 3 |
Week 4 (Oct 4) |
Backprop. (contd) |
4 |
Continue Lab Project 2 |
Week 5 (Oct 11) |
Bayes Classifier MLP training heuristcs |
1 4 |
Lecture 5 |
Week 6 (Oct 18) |
Radial Basis Function (RBF) Networks Lab Project 3: Radial Basis Functions |
5 |
Lecture 6 |
Week 7 (Oct 25) |
Fuzzy Neural Networks | Handout |
Lecture 7 |
Week 8 (Nov 1) |
Final
Project Proposal Due Midterm Exam |
In
Class + Take Home |
|
Week 9 (Nov 8) |
Feature Extraction Approximation Theory |
7 |
Lecture 9 |
Week 10 (Nov 15) |
Self Organizing Maps Hopfield Neural Networks Lab Project 4: Self Organizing Maps |
9 |
Lecture 10 |
Week 11 (Nov 22) |
Recurrent neural networks |
15 |
|
Week 12 (Nov 29) |
Final Project Presentations
|
||
Week 13 (Dec 6) |
Final Project Presentations
|
||
Week 14
(Dec 13) |
Final Project Presentations
|
||
Week 15
(Dec 20) |
Final Project Presentations
|
Instructor | Schedule | Textbook | Links | Grading | Homepage |