| Lec # | Topics | Key DATES |
|---|---|---|
| 1 | From Spikes to Rates | |
| 2 | Perceptrons: Simple and Multilayer | |
| 3 | Perceptrons as Models of Vision | |
| 4 | Linear Networks | Problem set 1 due |
| 5 | Retina | |
| 6 | Lateral Inhibition and Feature Selectivity | Problem set 2 due |
| 7 | Objectives and Optimization | Problem set 3 due |
| 8 | Hybrid Analog-Digital Computation Ring Network |
|
| 9 | Constraint Satisfaction Stereopsis |
Problem set 4 due |
| 10 | Bidirectional Perception | |
| 11 | Signal Reconstruction | Problem set 5 due |
| 12 | Hamiltonian Dynamics | |
| Midterm | ||
| 13 | Antisymmetric Networks | |
| 14 | Excitatory-Inhibitory Networks Learning |
|
| 15 | Associative Memory | |
| 16 | Models of Delay Activity Integrators |
Problem set 6 due one day after Lec #16 |
| 17 | Multistability Clustering |
|
| 18 | VQ PCA |
Problem set 7 due |
| 19 | More PCA Delta Rule |
Problem set 8 due |
| 20 | Conditioning Backpropagation |
|
| 21 | More Backpropagation | Problem set 9 due |
| 22 | Stochastic Gradient Descent | |
| 23 | Reinforcement Learning | Problem set 10 due |
| 24 | More Reinforcement Learning | |
| 25 | Final Review | |
| Final Exam |
