Statistical learning theory 2022 — различия между версиями
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| + | 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/ . | ||
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Sanjeev Kulkarni and Gilbert Harman: An Elementary Introduction to Statistical Learning Theory, 2012.--> | Sanjeev Kulkarni and Gilbert Harman: An Elementary Introduction to Statistical Learning Theory, 2012.--> | ||
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== Office hours == | == Office hours == | ||
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! Person !! Monday !! Tuesday !! Wednesday !! Thursday !! Friday !! | ! Person !! Monday !! Tuesday !! Wednesday !! Thursday !! Friday !! | ||
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| − | | | + | | Bruno Bauwens || || 14h--20h || || || || |
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| − | | | + | | Maxim Kaledin || || || || || || |
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Версия 17:01, 30 августа 2022
General Information
Lectures: Friday 16h20 -- 17h40, Bruno Bauwens, Maxim Kaledin
Seminars: Friday 18h10 -- 19h30, Artur Goldman,
For discussions of the materials, join the telegram group
The course is similar to 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
04 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 | |||||
| [2 Sept] | Lecture: philosophy. The online mistake bound model, the weighted majority, and perceptron algorithms movies | sl01 | ch00 ch01 | ||
| 9 Sept | The perceptron algorithm in the agnostic setting. Kernels. The standard optimal algorithm. | sl02 | ch02 ch03 | ||
| 16 Sept | Prediction with expert advice and the exponentially weighted majority algorithm. Recap probability theory. | sl03 | ch04 ch05 | ||
| Part 2. Distribution independent risk bounds | |||||
| 23 Sept | Sample complexity in the realizable setting, simple examples and bounds using VC-dimension | sl04 | ch06 | ||
| 30 Sept | Growth functions, VC-dimension and the characterization of sample comlexity with VC-dimensions | sl05 | ch07 ch08 | ||
| 7 Oct | Risk decomposition and the fundamental theorem of statistical learning theory | sl06 | ch09 | ||
| 14 Oct | Bounded differences inequality and Rademacher complexity | sl07 | ch10 ch11 | ||
| Part 3. Margin risk bounds with applications | |||||
| 21 Oct | Simple regression, support vector machines, margin risk bounds, and neural nets | sl08 | ch12 ch13 | ||
| 4 Nov | Kernels: risk bounds, RKHS, representer theorem, design | sl09 | ch14 | ||
| 11 Nov | AdaBoost and risk bounds | sl10 | Mohri et al, chapt 7 | ||
| Part 3. Other topics | |||||
| 18 Nov | Regression I | ||||
| 25 Nov | Regression II | ||||
| 2 Dec | Multiarmed bandids I | ||||
| 9 Dec | Multiarmed bandids II | ||||
| 16 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/ .
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)
Office hours
| Person | Monday | Tuesday | Wednesday | Thursday | Friday | |
|---|---|---|---|---|---|---|
| Bruno Bauwens | 14h--20h | |||||
| Maxim Kaledin |
It is always good to send an email in advance. Questions and feedback are welcome.
