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 2017S.
All exercise sessions will be held on Thursdays at 16:15 Liivi-224 and Fridays 12:15 Liivi 2-207
- 09.02: 1. Decision trees and association rules
- Session: GNU R and its usage by Sven Laur
- Self-study: Decision trees and association rules
- 16.02: 2. Linear regression by Sven Laur
- 23.02: 3. Performance measures by Sven Laur
- 02.03: 5. Linear classification by Sven Laur
- Self-study: Scalar product and matrix algebra
- 09.03: 4. Optimization basics by Ilya Kuzovkin
- 16.03: 6. Neural networks by Sven Laur
- 23.03: 7. Basics of probabilistic modelling by Sven Laur
- 30.03: 8. Maximum likelihood and maximum a posteriori estimates by Sven Laur
- 06.04: 9. Principle Component Analysis by Sven Laur
- 13.04: 10. Model-based clustering by Sven Laur
- 20.04: 11. Expectation-Maximisation algorithm by Sven Laur
- 27.04: 12. Support Vector Machines by Sven Laur
- 04.05: 13. Kernel Methods by Sven Laur
- 11.05: 14. Statistical Learning Theory by Sven Laur
- 18.05: 15. Ensemble Methods by Meelis Kull