Weeks | Topics |
1 |
Introduction, Neuron Model and Network Architectures
|
2 |
Perceptron Learning Rule
|
3 |
Signal and Weight Vector Spaces, Linear Transformations for Neural Networks
|
4 |
Supervised Hebbian Learning
|
5 |
Performance Optimization
|
6 |
Widrow-Hoff Learning
|
7 |
Backpropagation
|
8 |
Variations on Backpropagation (Drawbacks of Backpropagation, Heuristic Modifications of Backpropagation, Momentum,Conjugate Gradient,Levenberg-Marquardt Algorithm)
|
9 |
Midterm
|
10 |
Generalization
|
11 |
Dynamic Networks
|
12 |
Associative Learning (Kohonen Rule)
|
13 |
Radial Basis Networks
|
14 |
Project Demonstrations
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