Statistical learning theory 2021 — различия между версиями
Bbauwens (обсуждение | вклад) |
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|| [https://www.dropbox.com/s/py43d5k4mr7rv26/11sem.pdf?dl=0 Problem list 11] | || [https://www.dropbox.com/s/py43d5k4mr7rv26/11sem.pdf?dl=0 Problem list 11] | ||
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| 5 Dec || Recap of requested topics, Q&A | | 5 Dec || Recap of requested topics, Q&A | ||
Версия 14:34, 16 декабря 2022
Содержание
General Information
Teachers: Bruno Bauwens and Nikita Lukianenko
Lectures: Saturday 14:40 - 16:00. The lectures are in zoom.
Seminars: Tuesday 16:20 - 17:40. The seminars are here in google.meet.
Practical information on a telegram group.
The course is similar last year, except for the order of topics and part 3.
Problems exam
Dec 22, 12:00 -- 15:30
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)
Colloquium
Saturday December 11
rules and list of questions (version Dec 10)
Homeworks
Email to brbauwens-at-gmail.com. Start the subject line with SLT-HW. Results
Deadline before the lecture, every other lecture.
25 Sept: see problem lists 1 and 2
09 Oct: see problem lists 3 and 4
29 Oct: see problem lists 5 and 6
13 Nov: see problem lists 7 and 8
30 Nov, 08:00 [extended]: see problem lists 9 and 10
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 | |||||
| 5 Dec | Recap of requested topics, Q&A | Q&A |
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 | |
|---|---|---|---|---|---|---|
| Bruno Bauwens, Zoom | 12h30-14h30 | 14h-20h | Room S834 Pokrovkaya 11 | |||
| Nikita Lukianenko, Telegram | 14h30-16h30 | 14h30-16h30 | Room S831 Pokrovkaya 11 |
It is always good to send an email in advance. Questions and feedback are welcome.
