Before You Submit Exercises
- Requirements to solutions of home exercises!
- Nominal score for the homework is 10 points. You can earn up to 15 points for each homework!
- Your points will appear in this table Machine Learning 2014S.
Exercise Sessions
- 12.02: 1. GNU R and its usage given by Sven Laur
- 19.02: 2. Decision trees and association rules given by Sven Laur
- 26.02: 3. Linear regression given by Sven Laur
- 12.03: 4. Performance Measures given by Sven Laur
- 19.03: 5. Optimization basics given by Konstantin Tretyakov
- 26.03: 6. Linear classification given by Konstantin Tretyakov
- 02.04: 7. Neural networks given by Sven Laur
- 09.04: 8. Basics of probabilistic modelling given by Sven Laur
- 16.04: 9. Maximum likelihood and maximum a posteriori estimates given by Ilya Kuzovkin
- 23.04: 10. Principle Component Analysis given by Ilya Kuzovkin
- 30.04: 11. Model-based clustering given by Sven Laur
- 07.05: 12. Expectation-Maximisation algorithm given by Sven Laur
- 14.05: 13. Support Vector Machines given by Konstantin Tretyakov
- 21.05: 14. Statistical Learning Theory given by Sven Laur
- 28.05: 15. Kernel Methods given by Konstantin Tretyakov