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 |
Calendar
Course Info
Instructor
Departments
As Taught In
Spring
2005
Level
Learning Resource Types
notes
Lecture Notes
assignment
Problem Sets