Course plan

Lectures

  1. Introduction

  2. One layer perceptron

  3. Multi layer perceptron

  4. Multi layer perceptron (cont), Generalization

  5. Principal component analysis, independent component analysis

  6. Self organizing maps, vector quantization

  7. Boltzmann machines, Hopfield nets

  8. Processing temporal data, inverse models,

    Reinforcement learning

  9. Regularization, radial basis functions

  10. Ensemble computing, domain assumptions, representation

  11. Your questions. Old exam questions.