Statistical learning theory 2022 — различия между версиями
Bbauwens (обсуждение | вклад) |
Bbauwens (обсуждение | вклад) |
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For practical information, join the [https://t.me/+G0VKOE2-nnkwNDE0 telegram group] | For practical information, join the [https://t.me/+G0VKOE2-nnkwNDE0 telegram group] | ||
| − | The course is similar to the[http://wiki.cs.hse.ru/Statistical_learning_theory_2022 last year] | + | The course is similar to the [http://wiki.cs.hse.ru/Statistical_learning_theory_2022 last year]. |
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== Homeworks == | == Homeworks == | ||
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Deadline before the lecture, every other lecture. | Deadline before the lecture, every other lecture. | ||
| − | + | 23 Sept: see problem lists 1 and 2 <br> | |
| − | + | 07 Oct: see problem lists 3 and 4 <br> | |
| − | + | 21 Oct: see problem lists 5 and 6 <br> | |
| − | + | 4 Nov: see problem list 7 <br> | |
| − | + | 18 Nov: see problem lists 8 and 9 <br> | |
| + | 02 Dec: see problem lists 10 and 11 <br> | ||
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== Course materials == | == Course materials == | ||
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| + | == Problems exam == | ||
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| + | Dates, problems TBA | ||
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| + | During the exam<br> | ||
| + | -- You may consult notes, books and search on the internet <br> | ||
| + | -- You may not interact with other humans (e.g. by phone, forums, etc) | ||
Версия 16:33, 30 августа 2022
General Information
Lectures: Friday 16h20 -- 17h40, Bruno Bauwens, Maxim Kaledin
Seminars: Friday 18h10 -- 19h30, Artur Goldman,
For practical information, join the telegram group
The course is similar to the last year.
Homeworks
Email to brbauwens-at-gmail.com. Start the subject line with SLT-HW.
Deadline before the lecture, every other lecture.
23 Sept: see problem lists 1 and 2
07 Oct: see problem lists 3 and 4
21 Oct: see problem lists 5 and 6
4 Nov: see problem list 7
18 Nov: see problem lists 8 and 9
02 Dec: see problem lists 10 and 11
Course materials
| Video | Summary | Slides | Lecture notes | Problem list | Solutions |
|---|---|---|---|---|---|
| Part 1. Online learning | |||||
| 4 Sept | Lecture: philosophy. Seminar: the online mistake bound model, the weighted majority, and perceptron algorithms movies | sl01 | ch00 ch01 | 01prob (9 Sept) | 01sol |
| 11 Sept | The perceptron algorithm in the agnostic setting. Kernels. The standard optimal algorithm. | sl02 | ch02 ch03 | 02prob (23 Sept) | 02sol |
| 18 Sept (rec to do) | Prediction with expert advice and the exponentially weighted majority algorithm. Recap probability theory. | sl03 | ch04 ch05 | 03prob(30 Sept) | 03sol |
| Part 2. Risk bounds for binary classification | |||||
| 25 Sept | Sample complexity in the realizable setting, simple examples and bounds using VC-dimension | sl04 | ch06 | 04prob | 04sol |
| 2 Oct | Growth functions, VC-dimension and the characterization of sample comlexity with VC-dimensions | sl05 | ch07 ch08 | 05prob | 05sol |
| 9 Oct | Risk decomposition and the fundamental theorem of statistical learning theory | sl06 | ch09 | 06prob | 06sol |
| 16 Oct | Bounded differences inequality and Rademacher complexity | sl07 | ch10 ch11 | 07prob | 07sol |
| 30 Oct | Simple regression, support vector machines, margin risk bounds, and neural nets | sl08 | ch12 ch13 | 08prob | 08sol |
| 6 Nov | Kernels: risk bounds, RKHS, representer theorem, design | sl09 | ch14 | 09prob (Nov 23) | 09sol |
| 13 Nov | AdaBoost and risk bounds | sl10 | Mohri et al, chapt 7 | 10prob (Nov 23) | 10sol |
| Part 3. Other topics | |||||
| 20 Nov | Clustering | sl11 | Mohri et al, ch7; lecture | 11prob | 11sol |
| 27 Nov | Dimensionality reduction and the Johnson-Lindenstrauss lemma | sl12 | Mohri et al, ch15; lecture | 12prob | |
| 4 Dec | No lecture | ||||
| 11 Dec | Colloquium |
Problems exam
Dates, problems TBA
During the exam
-- You may consult notes, books and search on the internet
-- You may not interact with other humans (e.g. by phone, forums, etc)
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 http://gen.lib.rus.ec/ .
Office hours
| Person | Monday | Tuesday | Wednesday | Thursday | Friday | |
|---|---|---|---|---|---|---|
| , TBA | ||||||
| , TBA |
It is always good to send an email in advance. Questions and feedback are welcome.
