Statistical learning theory 2021 — различия между версиями
Bbauwens (обсуждение | вклад) |
Bbauwens (обсуждение | вклад) |
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== Homeworks == | == Homeworks == | ||
− | Email to brbauwens-at-gmail.com. Start the subject line with SLT-HW. [https://www.dropbox.com/s/ | + | Email to brbauwens-at-gmail.com. Start the subject line with SLT-HW. [https://www.dropbox.com/s/prsmzhtr5p5uome/scores.pdf?dl=0 Results] |
Deadline before the lecture, every other lecture. | Deadline before the lecture, every other lecture. |
Версия 13:10, 14 декабря 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.
Practical information on a telegram group. Join here.
The course is similar last year, except for the order of topics and part 3.
Colloquium
Saturday December 11
rules and list of questions (Update Dec 9, tickets + extra info, Dec 10 more info on questions)
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 |
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.