Introduction to Machine Learning (2018  2019)
Summary
Statistical machine learning is a growing discipline at the intersection
of computer science and applied mathematics (probability / statistics,
optimization, etc.) and which increasingly plays an important role in technological
innovation.
Unlike a course on traditional statistics, statistical machine learning
is particularly focused on the analysis of data in high dimension, as well
as the efficiency of algorithms to process the large amount of data encountered
in multiple application areas such as image or sound analysis, natural
language processing, bioinformatics or finance.
The objective of this class is to present the main theories and algorithms
in statistical machine learning. The methods covered will rely amongst
others on convex analysis arguments. The practical sessions (more than
half of which will be realized with computers) will lead to simple implementations
of the algorithms seen in class and with applications to various domains
such as computer vision or natural language processing.
Prerequisite: probability theory (notion of random variables, convergence
of random variables, conditional expectation), coding skills in python.
General information
This class is part of the Computer science courses taught at ENS in L3
in Spring 20182019.
Teachers:
Pierre Gaillard and
Alessandro Rudi.
Practical sessions:
Raphaël Berthier.
The class will last 52 hours (30 hours of class + 22 hours of practical
sessions) and
can be validated for 9 ECTS.
Final grade: 50% final exam, 50% homework.
Previous years:
Fall 2018,
2017,
2016,
2015,
2014,
2013,
2012
Schedule and lecture notes
Tuesday mornings from 8h30 to 12h30
in room UV. Typical session
will be a lecture from 8h30 to 10h20, followed by a 20min break and the
practical work (PW) from 10h40 to 12h30.
Bring your personal laptops in
practical sessions! Lecture notes and solutions to practical work and exercises
will be updated here on the fly.
# 
Date 
Teacher 
Title 
1 
05/02/2019 
A. Rudi
P. Gaillard 
Introduction
TD0 (Python test file) 
2 
12/02/2019 
P. Gaillard
R. Berthier 
Linear regression
TD1 (Data: classificationA_train, classificationA_test, classificationB_train, classificationB_test,
classificationC_train, classificationC_test, mnist_digits.mat) 
3 
19/02/2019 
A. Rudi
R. Berthier 
Statistical properties in ML 
4 
26/02/2019 
A. Rudi
R. Berthier 
KNN
TD2 

05/03/2019 

Vacation

5 
12/03/2019 
P. Gaillard
R. Berthier 
Logistic regression and convex analysis
TD3 
6 
19/03/2019 
A. Rudi
R. Berthier 
Convex optimization (good slides from AurĂ©lien Garivier, GD smooth and strongly convex, SGD)
TD4 

26/03/2019 

No class 
7 
02/04/2019 
P. Gaillard
R. Berthier 
High dimensional statistics
TD4 
8 
09/04/2019 
P. Gaillard
R. Berthier 
Model based machine learning: maximum likelihood
TD5 
9 
16/04/2019 
A. Rudi
R. Berthier 
Kernels (good notes from Arthur Gretton, sections 1, 2, 6)


23/04/2019 

Vacation 

30/04/2019 

Vacation 
10 
07/05/2019 
P. Gaillard
R. Berthier 
Unsupervised learning
TD7
 first assignment due date 
11 
14/05/2019 
A. Rudi
R. Berthier 
Neural networks
TD8

12 
21/05/2019 
A. Rudi
R. Berthier 
Summary
Last semester exam, solution  second assignment due date 
13 
28/05/2019 
P. Gaillard 
Exam 