- All practice session materials are available in Github
- Some of them may be in incomplete state. I will let you know which materials are stable
Points for the homeworks will appear in the table Machine Learning II 2019.
- 15.02 - 22.02: I. Performance measures
- Empirical risk and its variability
- Convergence of empirical risk to true risk
- Bias-variance trade-off in performance estimation
- Applications of crossvalidation algorithms
- Confidence intervals for crossvalidation estimates
- Different flavours of bootstrapping algorithms
- You can get up to 15 points from the homework exercises.
- Nominal score for the homework is 10 points.
- There are no restrictions how you choose exercises.
Solutions for this task can no longer be submitted.
- 01.03 - 08.03: II. Basics of probabilistic modelling
- True meaning of confidence intervals
- Quantile-Quantile plot and its confidence envelope
- Probability distributions over real-valued functions
- Confidence envelopes for predictors
- Confidence envelopes for ROC curves
- Bayesian inference and its internal consistency
- Naive-Bayes classification
- Markov chains and detection of abnormal words
Solutions for this task can no longer be submitted.
- 15.03 - 22.03: III. Graphical models and belief propagation
Solutions for this task can no longer be submitted.
- 29.03 - 16.04: IV. Normal distributions and linear projections
Solutions for this task can no longer be submitted.
- 24.04 - 03.05: V. Model-based clustering
Solutions for this task can no longer be submitted.
- 05.05 - 24.05: VI. Expectation-Maximisation algorithm Subdirectories 07 (nominal 10 points) and 08 (nominal 5 points)
Solutions for this task can no longer be submitted.
Ignore things below this marker. It is under construction
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