Introduction to Machine Learning (2020 - 2021)


MANDATORY INSCRIPTION


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 2020-2021.

Teachers: Alessandro Rudi and Francis Bach.
Practical sessions: Raphaël Berthier.

The class will last 52 hours (30 hours of class + 22 hours of practical sessions) and can be validated for 12 ECTS.
Final rade: approximately 50% final exam, 50% homework.

Previous years: Spring 2020, Spring 2019, Fall 2018, 2017, 2016, 2015, 2014, 2013, 2012


Schedule and lecture notes

Thursday mornings from 8h30 to 12h15 online on ZOOM. Typical session will be a lecture from 8h30 to 10h20, followed by a 20min break and the practical work (PW) from 10h40 to 12h15. Lecture notes and solutions to practical work and exercises will be updated here on the fly.


# Date Teacher Title
1 04/02/2021 F. Bach Introduction
2 11/02/2021 F. Bach
R. Berthier
Supervised learning and linear regression
TD1 (Data: classificationA_train, classificationA_test, classificationB_train, classificationB_test, classificationC_train, classificationC_test, mnist_digits.mat, solution, NEW: all in one zip: ALL)
3 18/02/2021 F. Bach
R. Berthier
Unsupervised Learning

25/02/2021
No Class
4 04/03/2021 A. Rudi
R. Berthier
Logistic regression and convex analysis
TD3, solution
5 11/03/2021 A. Rudi
R. Berthier
Convex optimization
TD4, solution to theoretical questions, solution to practical questions
6 18/03/2021 F. Bach
R. Berthier
High dimensional statistics (Lasso)
Practical session on SGD: TD5, data, solution
7 25/03/2021 F. Bach
R. Berthier
Model based machine learning: maximum likelihood
8 01/04/2021 A. Rudi
R. Berthier
Kernels
Exercise sheet, solution
9 08/04/2021 A. Rudi
R. Berthier
Elements of Statistical Machine Learning
Numerical tour of Ridge and Lasso by Gabriel Peyre
10 15/04/2021 A. Rudi
R. Berthier
Local methods
Python exercise , solution

22/04/2021
No Class

29/04/2021
No Class
11 06/05/2021 A. Rudi
R. Berthier
Neural networks
TP Neural Nets, solution

13/05/2021
No Class
12 20/05/2021 F. Bach Summary
13 27/05/2021 A. Rudi Exam