Statistical learning theory 2026
Дополнительные действия
General Information
Lectures: on ?? in Pokrovkaya, (weekly, for 80 min) see here for the room a few hours before the lecture, and in zoom by Bruno Bauwens
Seminars: on ?? (weekly for 80 min) online in Zoom by Nikita Lukianenko.
Please join the telegram group [to do]. The course is similar to last year.
Course materials
| Video | Summary | Slides | Lecture notes | Problem list | Solutions |
|---|---|---|---|---|---|
| Part 1. Online learning | |||||
| 16 Sep | Philosophy. The online mistake bound model. The halving and weighted majority algorithms. | sl01 | ch00 ch01 | prob01 | sol01 |
| 23 Sep | The standard optimal algorithm. The perceptron algorithm. | sl02 | ch02 ch03 | prob02 | sol02 |
| 30 Sep | Prediction with expert advice. Recap probability theory (seminar). | sl03 | ch04 ch05 | prob03 Upd 7 Oct | sol03 |
| Part 2. Distribution independent risk bounds | |||||
| 07 Oct | Necessity of a hypothesis class. Sample complexity in the realizable setting, examples: threshold functions and finite classes. | sl04 | ch06 | prob04 | sol04 |
| 14 Oct | Growth functions, VC-dimension and the characterization of sample comlexity with VC-dimensions | sl05 | ch07 ch08 | prob05 | sol05 |
| 21 Oct | Risk decomposition and the fundamental theorem of statistical learning theory (previous recording covers more) | sl06 | ch09 | prob06 | sol06 |
| 23 Oct | Bounded differences inequality, Rademacher complexity, symmetrization, contraction lemma. | sl07 | ch10 ch11 | prob07 | sol07 |
| Part 3. Margin risk bounds with applications | |||||
| 06 Nov | Simple regression, support vector machines, margin risk bounds, and dropout in neural nets (switch to old recording for SVM stuff). | sl08 | ch12 ch13 | prob08 | sol08 |
| 11 Nov | Kernels: RKHS, representer theorem, risk bounds | sl09 | ch14 | prob09 | sol09 |
| 18 Nov | AdaBoost and the margin hypothesis | sl10 | ch15 | prob10 | sol10 |
| Part 4. Neural nets | |||||
| 25 Nov | Exponential (and cross entropy loss) find maximal margin solutions. Losses of neural nets are not locally convex. | ch16 | See next | ||
| 02 Dec | Lazy training and the neural tangent kernel in overparameterized nets. | ch17 | prob11 | sol11 | |
| 09 Dec | Finnish previous lecture. Optional: a label dependent risk bound for overparameterized nets. | ch18 | Consult 15.12 | ||
| 16 Dec | Colloquium Rules and questions. Select a timeslot. |
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.
A gentle introduction to the materials of the first 3 lectures and an overview of probability theory, can be found in chapters 1-6 and 11-12 of the following book: Sanjeev Kulkarni and Gilbert Harman: An Elementary Introduction to Statistical Learning Theory, 2012.
Grading formula
Final grade = 0.3 * [avg score of intermediate exams] + 0.35 * [score of 2 colloquiums] + 0.2 * [score on the exam] + bonus from quizzes.
There are are 2 colloquiums, 1 during each of the sessions. There are 3 intermediate exams, at the end of September, during the session at the end of Okt, at the end of Nov.
At the end of the lectures there is a short quiz in which you may earn 0.1 bonus points on the final non-rounded grade.
There is no rounding except for transforming the final grade to the official grade. Arithmetic rounding is used.
Autogrades: if you only need 6/10 on the final exam to have the maximal 10/10 for the course, this will be given automatically.
Problems exam
Date: Saturday 20.12, 13h-17h, room D203
-- You may use handwritten notes, lecture materials from this wiki (either printed or through your PC, it is a computer room), Mohri's book
-- You may not search on the internet or interact with other humans (e.g. by phone, forums, etc)
About questions
-- 4 or 5 questions of the difficulty of the homework. (Many homework questions were from former exams.)
-- I always ask to calculate VC dimension and to give/prove some risk bound with Rademacher complexity.
-- Example of an exam (a bit easier, during COVID).
If you have a passing grade without attending the exam, you may skip the exam and I will mark you as present by default.
Office hours
Bruno Bauwens: Tuesday 15h-21h Better send me an email in advance.
Nikita Lukianenko: Write in Telegram, the time is flexible