Lectures
All lectures will be held on Wednesdays at 10:15 Liivi-207
- All lecture materials are available in Github
- Some of them may be in incomplete state. I will let you know which materials are stable
List of topics
- Performance evaluation measures
- Basics of probabilistic modelling
- Maximum likelihood and maximum a posteriori estimates
- Principal Component Analysis
- Autoecoders
- Model-based clustering
- Expectation-maximisation algorithm
- Data augmentation
- Graphical models and knowledge representation
Ignore things below this marker. It is under construction
- 15.02: Linear models and polynomial interpolation by Sven Laur
- 22.02: Performance evaluation measures by Sven Laur
- 29.02: Introduction to optimization by Ilya Kuzovkin
- 07.03: Linear classification by Sven Laur
- 14.03: Neural networks by Ilya Kuzovkin
- 21.03: Basics of probabilistic modelling by Sven Laur
- 28.03: Maximum likelihood and maximum a posteriori estimates by Sven Laur
- 04.04: Principal Component Analysis by Sven Laur
- 11.04: Model-based clustering by Sven Laur
- 18.04: Expectation-maximisation algorithm by Sven Laur
- 25.04: Support Vector Machines by Sven Laur
- 02.05: Kernel Methods by Sven Laur
- 09.05: Elements of Statistical Learning Theory by Sven Laur
- 16.05: Ensemble Methods by Meelis Kull