Statistical learning theory 2020 — различия между версиями
Bbauwens (обсуждение | вклад) |
Bbauwens (обсуждение | вклад) |
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| 10 Oct || PSupport vector machines and risk bounds | | 10 Oct || PSupport vector machines and risk bounds | ||
− | || | + | || Chapt 5, Mohri et al, see below |
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Версия 18:20, 9 октября 2020
General Information
Teachers: Bruno Bauwens and Vladimir Podolskii
Lectures: Saturday 9h30 - 10h50, zoom https://zoom.us/j/96210489901
Seminars
- Group 1: Saturday 11h10 - 12h30, Bruno Bauwens and Vladimir Podolskii zoom https://zoom.us/j/94186131884,
- Group 2: Tuesday 18h, Nikita Lukyanenko, see ruz.hse.ru
Practical information on telegram group
Homeworks: deadlines every 2 weeks, before the lecture.
- Sat 3 Oct: see problem lists 1 (updated) and 2
- Sat 17 Oct: see problem lists 3 and 4
- Sat 31 Oct: see problem lists 5 and 6
- Sat 14 Nov: see problem lists 6 and 7
- Sat 28 Nov: see problem lists 8 and 9
Email homeworks to Brbauwens <at> gmail.com. Start the subject line with SLT-HW. You may submit handwritten solutions.
Course materials
Date | Summary | Lecture notes | Slides | Video | Problem list | Solutions |
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12 Sept | Introduction and sample complexity in the realizable setting | lecture1.pdf | slides1.pdf | Problem list 1 Update 26.09, prob 1.7 | Solutions 1 | |
19 Sept | VC-dimension and sample complexity | lecture2.pdf | slides2.pdf | Chapt 2,3 | Problem list 2 | Solutions 2 |
26 Sept | Risk bounds and the fundamental theorem of statistical learning theory | lecture3.pdf | slides3.pdf | Problem list 3 | Solutions 3 | |
03 Oct | Rademacher complexity | lecture4.pdf | slides4.pdf | Problem list 4 Update 09.10, prob 4.3,4.6 | Solutions 4 | |
10 Oct | PSupport vector machines and risk bounds | Chapt 5, Mohri et al, see below | ||||
17 Oct | Support vector machines and kernels. | |||||
31 Oct | Adaboost | |||||
07 Nov | Online learning 1 | |||||
14 Nov | Online learning 2 | |||||
21 Nov | Active learning | |||||
28 Nov | Unsupervised learning | |||||
5 Dec | Optional: Neural networks and stochastic gradient descent | |||||
12 Dec | Optional: Neural networks and stochastic gradient descent |
The lectures in October are based on the book: Foundations of machine learning 2nd ed, Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalker, 2018. These books can be downloaded from http://gen.lib.rus.ec/ .
In November, we follow the lecture notes by Н. К. Животовский
Office hours
Person | Monday | Tuesday | Wednesday | Thursday | Friday | |
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Bruno Bauwens | 14h-18h | 16h15-20h | Room S834 Pokrovkaya 11 |
It is always good to send an email in advance. Questions are welcome.