Statistical learning theory 2021 — различия между версиями
Bbauwens (обсуждение | вклад) |
Bbauwens (обсуждение | вклад) |
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| Строка 106: | Строка 106: | ||
| 13 Nov | | 13 Nov | ||
|| AdaBoost and risk bounds | || AdaBoost and risk bounds | ||
| − | || [https://www.dropbox.com/s/ | + | || [https://www.dropbox.com/s/kc89rogi6cd8v44/10allSlides.pdf?dl=0 sl10] |
|| Mohri et al, chapt 7 | || Mohri et al, chapt 7 | ||
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Версия 14:35, 13 ноября 2021
General Information
Teachers: Bruno Bauwens and Nikita Lukianenko
Lectures: Saturday 14:40 - 16:00. The lectures are here in zoom.
Seminars: Tuesday 16:20 - 17:40. The seminars are here in google.meet.
See ruz for the rooms.
Practical information on a telegram group. Join here.
The course is similar last year, except for the order of topics and part 3.
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
27 Nov: see problem lists 9 and 10
11 Dec: see problem lists 11 and 12
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 | 01sl | 00ch 01ch | 01prob (9 Sept) | 01sol |
| 11 Sept | The perceptron algorithm in the agnostic setting. Kernels. The standard optimal algorithm. | 02sl | 02ch 03ch | 02prob (23 Sept) | 02sol |
| 18 Sept (rec to do) | Prediction with expert advice and the exponentially weighted majority algorithm. Recap probability theory. | 03sl | 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 | prob08 | sol08 |
| 6 Nov | Kernels: risk bounds, RKHS, representer theorem, design | sl09 | ch14 | prob09 | sol09 |
| 13 Nov | AdaBoost and risk bounds | sl10 | Mohri et al, chapt 7 | ||
| Part 3. Other topics | |||||
| 20 Nov | Clustering | ||||
| 27 Nov | Dimensionality reduction and the Johnson-Lindenstrauss lemma | ||||
| 4 Dec | No lecture | ||||
| 11 Dec | Colloquium |
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.
I am traveling from Sept 12 -- Sept 30 and Oct 16 -- Oct 26. On Fridays I'm available till 16h30.
