This semester Terry Sejnowski is teaching a graduate seminar course that is focused on Deep Learning. The course meets weekly for two hours to discuss papers. Here I’ll just outline the course and in later posts I’ll add some thoughts on each specific week.

## Week 1: Perceptrons

- Rosenblatt, F. A comparison of several perceptron models, Yovits, Jacobi, Goldstein (Eds.), Self-Organizing Systems, Spartan Books, New York (1962)
- Gray, M. S. Lawrence, D. T. Golomb, B. A. Sejnowski, T. J. A Perceptron Reveals the Face of Sex, Neural Computation, 7, 1160-1164, 1995
- Pollack, JB, Book Review, Perceptrons.

## Week 2: Hopfield Nets and Boltzmann Machines

## Week 3: Backprop

## Week 4: Independent Component Analysis (ICA)

## Week 5: Convolutional Neural Networks (CNN)

## Week 6: Recurrent Neural Networks (RNN)

## Week 7: Reinforcement Learning

## Week 8: Information and Control Theory

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