Detailed Contents
Will be updated during the semester.
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 Central Concepts in Machine Learning
Slides: Central Concepts in Machine Learning (PDF)
1.3 Estimators
Slides: Estimators
(PDF)
1.4 Decision Theory
Slides: Decision Theory
(PDF)
2. Linear Models for Classification
Slides: Discriminant
functions, Generative models, Logistic regression, ... (PDF)
3. Gradient Descent
Slides: Gradient
Descent (PDF, update 13.11.2017)
4. Neural Networks
Slides: Neural Networks (PDF)
Slides: Symbolic
Derivatives (PDF)
Slides: Basic
Training Algorithms (PDF)
Slides: Validation
and Regularization (PDF)
Slides: Convolutional
networks (PDF)
5. Recurrent Neural Networks
Slides: Recurrent
Neural Networks (PDF)
6. Boltzmann Machines
Slides: Boltzmann
Machines, Restricted Boltzmann Machines, Wake-Sleep, Contrastive Divergence
(PDF)