Statistical learning theory 2021

General Information

Grading

Teachers: Bruno Bauwens and Nikita Lukianenko

Lectures: Tuesdays 9h30 - 10h50, zoom

Seminars: Tuesday 11h10 - 12h30

Practical information on telegram group

Course materials

Date	Summary	Lecture notes	Problem list	Solutions
	Part 1. Online learning
7 Sept	Introduction, the online mistake bound model, the weighted majority and perceptron algorithms
14 Sept	The standard optimal algorithm, prediction with expert advice, exponentially weighted algorithm
21 Sept	Better mistake bounds using VC-dimensions. Recap probability theory. Leave on out risk for SVM.
	Part 2. Supervised classification
28 Sept	Sample complexity in the realizable setting, simple example and bounds using VC-dimension
5 Oct	Risk decomposition and the fundamental theorem of statistical learning theory
12 Oct	Rademacher complexity
26 Oct	Support vector machines and margin risk bounds
2 Nov	AdaBoost and risk bounds
Part 3. Other topics
9 Nov	Clustering
16 Nov	Dimensionality reduction and the Johnson-Lindenstrauss lemma

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/ .

For online learning, we also study a few topics from lecture notes by Н. К. Животовский

Office hours

Person	Monday	Tuesday	Wednesday	Thursday	Friday
Bruno Bauwens, Zoom (email in advance)	14h-18h	16h15-20h				Room S834 Pokrovkaya 11

It is always good to send an email in advance. Questions are welcome.