Statistical learning theory

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General Information

Syllabus for the 1st module

The intermediate exam will happen on Tuesday Okt. 31 for exercises and the colloquium (for theory questions) will be in smaller groups around this date.

Exams module 1

There are two exams.

Problems exam: Tuesday 31 Okt. 12h10-15h00: The score of your exam has weight 0.2 in your final grade. You solve exercises similar to the ones in the seminars. You can bring lecture notes, handwritten notes, and pages from Chapt 3, Sect. 4.4 and Chapt 6 from the book "Foundations of Machine Learning Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar".

Colloquium exam: This exam counts for 0.2 of your final grade. You will receive a lemma, proposition or theorem from the lecture notes (and a few topics from the seminars). You need to write the proof and the teacher will ask questions to check your understanding. A list with questions will be posted here.

БПМИ 141-1 || Wednesday 1st of November || 12h10-15h40 || БПМИ 141-2 || Wednesday 1st of November || 13h40-16h10 || БПМИ 142-1 || Wednesday 1st of November || 16h40-18h40 || БПМИ 142-2 || Wednesday 1st of November || 17h40-19h40 || БПМИ 143+145 || Thursday 2th of November || 15h10-17h10 || БПМИ 144 || Thursday 2th of November || 16h40-18h40 || 3th year || Friday 3th of November || 15h10-17h40 ||
Group Date Time Room

Your score of the homework has weight 0.1 in your final grade. Activities in the second module count for 0.5 of weight to the final grade.


Homework module 1

Course materials

Date Summary Lecture notes Problem list
5 sept PAC-learning and VC-dimension: definitions 1st and 2nd lecture Updated on 13th of Sept. Problem list 1
12 sept PAC-learning and VC-dimension: proof of fundamental theorem Problem list 2
19 sept Sauer's lemma, neural networks and agnostic PAC-learning 3th lecture Updated on the 23th of Sept. Problem list 3
26 sept Measure concentration, agnostic PAC-learning and Computational learning theory 4th lecture Problem list 4
3 okt Boosting: the adaBoost algorithm 5th lecture (part about agnostic learning) About boosting: see chapt 6 in the book of Mohri (see below) Problem list 5
10 okt Boosting: risk bounds using Rademacher complexities Mohri's book: p33-40, Talagrand's lemma, McDiarmid's inequality 6th lecture (Draft) Problem list 6
17 okt Margin theory and a deep boosting algorithm Mohri's book: p75-83, p131-136 (see the paper below)

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.

Foundations of machine learning, Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalker, 2012. These books can downloaded from .

(We will study a new boosting algorithm, based on the paper: Multi-class deep boosting, V. Kuznetsov, M Mohri, and U. Syed, Advances in Neural Information Processing Systems, p2501--2509, 2014. Notes will be provided.)

Office hours

Person Monday Tuesday Wednesday Thursday Friday
Bruno Bauwens 15:05–18:00 15:05–18:00 Room 620
Quentin Paris