9.641J | Spring 2005 | Graduate

Introduction to Neural Networks

Calendar

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

Course Info

As Taught In
Spring 2005
Level
Learning Resource Types
Lecture Notes
Problem Sets