Statistical learning theory 2023/24
Содержание
General Information
Lectures: Tuesday 14h40 -- 16h00, Bruno Bauwens, Maxim Kaledin, room S321 and in zoom
Seminars: TBA, Artur Goldman The first seminar will be on 11.09 in room D201, 9:30--10:50 and also in https://us02web.zoom.us/j/82300259484?pwd=NWxXekxBeE5yMm9UTmwvLzNNNGlnUT09 zoom].
To discuss the materials, join the telegram group The course is similar to last year.
Homeworks
Deadline every 2 weeks, before the seminar.
Email to brbauwens-at-gmail.com. Start the subject line with SLT-HW. -Link with results todo-.
Course materials
| Video | Summary | Slides | Lecture notes | Problem list | Solutions |
|---|---|---|---|---|---|
| Part 1. Online learning | |||||
| 05 Sept | Philosophy. The online mistake bound model. The halving and weighted majority algorithms movies | sl01 | ch00 ch01 | prob01 | |
| 12 Sept | The perceptron algorithm. Kernels. The standard optimal algorithm. | sl02 | ch02 ch03 | ||
| 19 Sept | Prediction with expert advice. Recap probability theory. Multi-armed bandids. | sl03 | ch04 ch05 | ||
| 26 Sept | Multi-armed bandids. | sl03 | ch04 ch05 | ||
| Part 2. Distribution independent risk bounds | |||||
| 03 Oct | Sample complexity in the realizable setting, simple examples and bounds using VC-dimension | sl04 | ch06 | ||
| 10 Oct | Growth functions, VC-dimension and the characterization of sample comlexity with VC-dimensions | sl05 | ch07 ch08 | ||
| 17 Oct | Risk decomposition and the fundamental theorem of statistical learning theory | sl06 | ch09 | ||
| 24 Oct | Bounded differences inequality, Rademacher complexity, symmetrization, contraction lemma, quiz | sl07 | ch10 ch11 | ||
| Part 3. Margin risk bounds with applications | |||||
| 07 Nov | Simple regression, support vector machines, margin risk bounds, and neural nets | sl08 | ch12 ch13 | ||
| 14 Nov | Kernels: RKHS, representer theorem, risk bounds | sl09 | ch14 | ||
| 21 Nov | AdaBoost and the margin hypothesis | sl10 | ch15 | ||
| 28 Nov | Implicit regularization of stochastic gradient descent in neural nets | ch16 | |||
| Part 4. Neural tangent kernels | |||||
| 05 Dec | Optional: part 1. | ||||
| 12 Dec | Colloquium | ||||
| 19 Dec | Optional: part 2. |
Background on multi-armed bandits: A. Slivkins, [Introduction to multi-armed bandits https://arxiv.org/pdf/1904.07272.pdf], 2022.
The lectures in October and November are based on the book: Foundations of machine learning 2nd ed, Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalker, 2018. This book can be downloaded from Library Genesis (the link changes sometimes and sometimes vpn is needed).
Grading formula
Final grade = 0.35 * [score of homeworks] + 0.35 * [score of colloquium] + 0.3 * [score on the exam] + bonus from quizzes.
All homework questions have the same weight. Each solved extra homework task increases the score of the final exam by 1 point.
There is no rounding except on the final grade. Grades fractional grades above 5/10 are rounded up, those below 5/10 are rounded down.
Autogrades: if you only need 4/10 to pass with maximal final score, it will be given automatically. This may happen because of extra questions and bonuses from quizzes.
Colloquium
Rules and questions of previous year.
Problems exam
December 21--30, TBA.
-- You may use handwritten notes, lecture materials from this wiki (either printed or through your PC), Mohri's book
-- You may not search on the internet or interact with other humans (e.g. by phone, forums, etc)
Office hours
Bruno Bauwens: Wednesday 13h-16h, Friday 14h-20h, (better send an email in advance).
Maxim Kaledin: Write in Telegram, the time is flexible
