Statistical learning theory 2020 — различия между версиями

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| 03 Nov || Rademacher complexity and margin assumption ||
 
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| 1 Oct || Agnostic PAC-learnability is equivalent with finite VC-dimension, structural risk minimization || [https://www.dropbox.com/s/jsrse5qaqk2jhi1/05lect.pdf?dl=0 lecture5.pdf] 14/10 || [https://www.dropbox.com/s/etw67uq1pu5g58t/05sem.pdf?dl=0 Problem list 5] || [https://www.dropbox.com/s/6mpom53yrldcrjy/05solution.pdf?dl=0 Solution 5]
 
| 1 Oct || Agnostic PAC-learnability is equivalent with finite VC-dimension, structural risk minimization || [https://www.dropbox.com/s/jsrse5qaqk2jhi1/05lect.pdf?dl=0 lecture5.pdf] 14/10 || [https://www.dropbox.com/s/etw67uq1pu5g58t/05sem.pdf?dl=0 Problem list 5] || [https://www.dropbox.com/s/6mpom53yrldcrjy/05solution.pdf?dl=0 Solution 5]
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== Russian texts  ==
 
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``Наука и искусство построения алгоритмов, которые извлекают знания из данных'', Петер Флах. On [http://www.machinelearning.ru machinelearning.ru]  
 
``Наука и искусство построения алгоритмов, которые извлекают знания из данных'', Петер Флах. On [http://www.machinelearning.ru machinelearning.ru]  
 
you can find brief and clear definitions.
 
you can find brief and clear definitions.
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Версия 18:22, 11 сентября 2020

General Information

Lectures: Saturday 9h30 - 10h50, zoom https://zoom.us/j/96210489901

Teachers: Bruno Bauwens and Vladimir Podolskii

Seminar for group 1: Saturday 11h10 - 12h30, Bruno Bauwens and Vladimir Podolskii zoom https://zoom.us/j/94186131884,

Seminar for group 2: Tuesday ??, Nikita Lukyanenko

Course materials

Date Summary Lecture notes Problem list Solutions
12 Sept Introduction and sample complexity in the realizable setting lecture1.pdf Problem list 1 Solutions 1
19 Sept VC-dimension and sample complexity
26 Sept Risk bounds and the fundamental theorem of statistical learning theory
03 Nov Rademacher complexity and margin assumption

<-- A gentle introduction to the materials of the first 3 lectures and an overview of probability theory, can be found in chapters 1-6 and 11-12 of the following book: Sanjeev Kulkarni and Gilbert Harman: An Elementary Introduction to Statistical Learning Theory, 2012.-->

<-- Afterward, we hope to cover chapters 1-8 from the book: Foundations of machine learning, Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalker, 2012. These books can be downloaded from http://gen.lib.rus.ec/ .

(We will study a new boosting algorithm, based on the paper: ) -->

Office hours

Person Monday Tuesday Wednesday Thursday Friday
Bruno Bauwens Room 620