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 2016S.
Exercise Sessions
All exercise sessions will be held on Tuesdays at 16:15 Liivi-202
- 09.02: 1. Decision trees and association rules
- Session: GNU R and its usage by Sven Laur
- Self-study: Decision trees and association rules (deadline 01.03 16:15)
- 16.02: 2. Linear regression by Sven Laur
- 23.02: 3. Performance measures by Sven Laur
- 01.03: 4. Optimization basics by Ilya Kuzovkin
- 08.03: 5. Linear classification by Sven Laur
- Self-study: Scalar product and matrix algebra (deadline 07.03 16:15)
- 15.03: 6. Neural networks by Sven Laur
- 22.03: 7. Basics of probabilistic modelling by Sven Laur
- 29.03: 8. Maximum likelihood and maximum a posteriori estimates by Sven Laur
- 05.04: 9. Principle Component Analysis by Sven Laur
- 12.04: 10. Model-based clustering by Sven Laur
- 19.04: 11. Expectation-Maximisation algorithm by Sven Laur
- 26.04: 12. Support Vector Machines by Sven Laur
- 03.05: 13. Kernel Methods by Sven Laur
- 10.05: 14. Statistical Learning Theory by Sven Laur
- 17.05: 15. Ensemble Methods by Meelis Kull