Sixth exercises posted online. By the end of week 5, we progressed to p. 40 on the lecture notes, although skipped the proof of Theorem 3.19.
Fifth exercises posted online. By the end of week 4, we progressed to p. 28 on the lecture notes.
Fourth exercises posted online. By the end of week 3, we progressed to p. 22 on the lecture notes.
Exercise 6 in the 3rd set has been removed and replaced by Exercise 4.
Third exercises posted online. By the end fo week 2, we progressed to p. 13 on the lecture notes.
Exercise 6 in the 2nd set has been removed and replaced by Exercise 3.
Second exercises posted online. By the end of week 1, we progressed to p. 9 on the lecture notes.
First lecture on Jan 16. Lecture times: Thursdays and Fridays at 12.15 in MaD355.
First exercises on Jan 23. Exercises on Thursdays at 14.15 in MaD355.
Familiarity with general probability spaces: you should know what the sentence "expectation is an integral" means before coming to the course. Appendix A of the lecture notes (see below) contains a summary of the probability theory we will use. If this is not familiar to you beforehand, be prepared for some extra self-study.
We will follow these lecture notes.
Further reading in the books Understanding Machine Learning by Ben-David and Shalev-Shwartz or Neural Network Learning by Anthony and Bartlett.
Note that you have to solve at least one third of the exercises to be admitted to the course exam.
First exercises (due Jan 23, Solutions by Max)
Second exercises (due Jan 30, Solutions by Max)
Third exercises (due Feb 6, Solutions by Max)
Fourth exercises (due Feb 13, Solutions by Max)
Fifth exercises (due Feb 20, Solutions by Max)
Sixth exercises (due Feb 27, Solutions by Max)
The exercise classes will be held by Max Goering. If you wish to return solutions in writing, you can e-mail them to Max directly.