Statistical learning theory 2024/25 — различия между версиями
Bauwens (обсуждение | вклад) |
Bauwens (обсуждение | вклад) |
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| Строка 72: | Строка 72: | ||
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| − | | [https://www.youtube.com/watch?v=yMsUH1brAs8 | + | | [https://www.youtube.com/watch?v=yMsUH1brAs8 05 Nov] |
|| Bounded differences inequality, Rademacher complexity, symmetrization, contraction lemma. | || Bounded differences inequality, Rademacher complexity, symmetrization, contraction lemma. | ||
|| [https://www.dropbox.com/s/kfithyq0dgcq6h8/07slides.pdf?dl=0 sl07] | || [https://www.dropbox.com/s/kfithyq0dgcq6h8/07slides.pdf?dl=0 sl07] | ||
| Строка 82: | Строка 82: | ||
|| ''Part 3. Margin risk bounds with applications'' | || ''Part 3. Margin risk bounds with applications'' | ||
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| − | | [https://www.youtube.com/watch?v=oU2AzubDXeo | + | | [https://www.youtube.com/watch?v=oU2AzubDXeo 12 Nov] |
|| Simple regression, support vector machines, margin risk bounds, and neural nets with dropout regularization | || Simple regression, support vector machines, margin risk bounds, and neural nets with dropout regularization | ||
|| [https://www.dropbox.com/s/oo1qny9busp3axn/08slides.pdf?dl=0 sl08] | || [https://www.dropbox.com/s/oo1qny9busp3axn/08slides.pdf?dl=0 sl08] | ||
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|- | |- | ||
| − | | [https://youtu.be/9FhFxLHR4eE | + | | [https://youtu.be/9FhFxLHR4eE 19 Nov] |
|| Kernels: RKHS, representer theorem, risk bounds | || Kernels: RKHS, representer theorem, risk bounds | ||
|| [https://www.dropbox.com/s/jst60ww8ev4ypie/09slides.pdf?dl=0 sl09] | || [https://www.dropbox.com/s/jst60ww8ev4ypie/09slides.pdf?dl=0 sl09] | ||
| Строка 96: | Строка 96: | ||
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|- | |- | ||
| − | | [https://www.youtube.com/watch?v=OgiaWrWh_WA | + | | [https://www.youtube.com/watch?v=OgiaWrWh_WA 26 Nov] |
|| AdaBoost and the margin hypothesis | || AdaBoost and the margin hypothesis | ||
|| [https://www.dropbox.com/s/umum3kd9439dt42/10slides.pdf?dl=0 sl10] | || [https://www.dropbox.com/s/umum3kd9439dt42/10slides.pdf?dl=0 sl10] | ||
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| − | | [https://youtu.be/GL574ljefJ8 | + | | [https://youtu.be/GL574ljefJ8 03 Dec] |
|| Implicit regularization of stochastic gradient descent in overparameterized neural nets ([https://www.youtube.com/watch?v=ygVHVW3y3wM recording] with many details about the Hessian) | || Implicit regularization of stochastic gradient descent in overparameterized neural nets ([https://www.youtube.com/watch?v=ygVHVW3y3wM recording] with many details about the Hessian) | ||
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| Строка 110: | Строка 110: | ||
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| − | | [https://www.youtube.com/watch?v=RDTK7hBqiJY | + | | [https://www.youtube.com/watch?v=RDTK7hBqiJY 10 Dec] |
|| Part 2 of previous lecture: Hessian control and stability of the NTK. | || Part 2 of previous lecture: Hessian control and stability of the NTK. | ||
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Версия 14:22, 14 октября 2024
Содержание
[убрать]General Information
Lectures: on Tuesday 9h30--10h50 in room M302 and in zoom by Bruno Bauwens
Seminars: online in Zoom by Nikita Lukianenko.
Please join the telegram group The course is similar to last year.
Homeworks
Deadline every 2 weeks, before the lecture. The tasks are at the end of each problem list. (Problem lists will be updated, check the year.)
Before 3rd lecture, submit HW from problem lists 1 and 2. (Thus Oct 1st, 9h30.) Before 5th lecture, from lists 3 and 4. Etc.
Classroom to submit homeworks. You may submit in English or Russian, as latex or as pictures. Results are here.
Late policy: 1 homework can be submitted at most 24 late without explanations.
Course materials
| Video | Summary | Slides | Lecture notes | Problem list | Solutions |
|---|---|---|---|---|---|
| Part 1. Online learning | |||||
| 21 Sep | Philosophy. The online mistake bound model. The halving and weighted majority algorithms. | sl01 | ch00 ch01 | prob01 | sol01 |
| 24 Sep | The standard optimal algorithm. The perceptron algorithm. | sl02 | ch02 ch03 | prob02 | sol02 |
| 01 Oct | Kernel perceptron algorithm. Prediction with expert advice. Recap probability theory (seminar). | sl03 | ch04 ch05 | prob03 | sol03 |
| Part 2. Distribution independent risk bounds | |||||
| 08 Oct | Necessity of a hypothesis class. Sample complexity in the realizable setting, examples: threshold functions and finite classes. | sl04 | ch06 | prob04 update 12.10 | sol04 |
| 15 Oct | Growth functions, VC-dimension and the characterization of sample comlexity with VC-dimensions | sl05 | ch07 ch08 | prob06 | |
| 22 Oct | Risk decomposition and the fundamental theorem of statistical learning theory | sl06 | ch09 | prob07 | |
| 05 Nov | Bounded differences inequality, Rademacher complexity, symmetrization, contraction lemma. | sl07 | ch10 ch11 | prob08 | |
| Part 3. Margin risk bounds with applications | |||||
| 12 Nov | Simple regression, support vector machines, margin risk bounds, and neural nets with dropout regularization | sl08 | ch12 ch13 | prob09 | |
| 19 Nov | Kernels: RKHS, representer theorem, risk bounds | sl09 | ch14 | prob10 | |
| 26 Nov | AdaBoost and the margin hypothesis | sl10 | ch15 | prob11 | |
| 03 Dec | Implicit regularization of stochastic gradient descent in overparameterized neural nets (recording with many details about the Hessian) | ch16 ch17 | |||
| 10 Dec | Part 2 of previous lecture: Hessian control and stability of the NTK. |
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 = 0.35 * [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. 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 exam to have the maximal 10/10 for the course, this will be given automatically. This may happen because of extra homework questions and bonuses from quizzes.
Colloquium
Rules and questions from last year.
Date: TBA
Problems exam
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: Bruno Bauwens: Tuesday 12h -- 20h. Wednesday 16h -- 18h. Friday 11h -- 17h. Better send me an email in advance.
Nikita Lukianenko: Write in Telegram, the time is flexible
