Lectures
- 10.02: Association rules and decision trees
- 17.02: Linear models and polynomial interpolation
- 10.03: Performance evaluation measures
- 17.03: Introduction to optimization
- 24.03: Linear classification
- 31.03: Neural networks
- 07.04: Basics of probabilistic modelling
- 14.04: Maximum likelihood and maximum a posteriori estimates
- 21.04: Principal Component Analysis
- 28.04: Model-based clustering
- 05.05: Expectation-maximisation algorithm
- 12.05: Support Vector Machines
- 19.05: Elements of Statistical Learning Theory
- 26.05: Kernel Methods