Detailed Contents

Will be updated during the semester. (You need a password to obtain the files. If you are an NNB student, you gan get it from Robert Legenstein). The slides used in the lecture will be placed here.

1. Introduction

Slides: Introduction (PDF)

1.1 Pattern Recognition

Slides: Pattern Recognition (PDF)

1.2 A Simple Example

Slides: Introductory Example (PDF)

1.3 A Probabilistic Perspective

Slides: Probabilistic Perspective (PDF)

2. Linear Models for Regression

Slides: Least squares, LMS (PDF)

3. Linear Models for Classification

Slides: Fisher's linear discriminant, Delta rule, logistic regression ... (PDF)

4. Neural Networks

Slides: Neural Networks (PDF)

5. Neural Networks - Practical Considerations

Slides: Practical Considerations (PDF)

6. RBF Networks

Slides: RBF Networks (PDF)

7. Boltzmann Machines, Deep Belief Networks

Slides: Boltzmann Machines, Deep Networks (PDF)

DBN in action on Hintons homepage.

8. Simulated Annealing

Slides: Simulated Annealing (Markov chains, MCMC) (PDF)

9. The Bias-Variance Decomposition, Bayesian Linear Regression

Slides: The Bias-Variance Decomposition, Bayesian Linear Regression (PDF)