MVA Course - Introduction to Statistical Learning

2020/2021

***Instructors:
Prof.
Nicolas VAYATIS
TA : Marie Garin, Batiste Le Bars

***Emails:
<firstname.name/-at-/ens-paris-saclay.fr>

***Syllabus 2020-2021

***Course schedule and location:

Amphi Lagrange 1Z14, ENS Paris-Saclay --> how to get there
!!Special time and location for
Exercise session #2!!

Date Time Instructor Session Topics Files/Readings
Tuesday October 6
11:00-13:00 N. Vayatis Lecture #1 Course introduction and main setup
Chapter 1 - Optimality in statistical learning
Slides
Tuesday October 13
11:00-13:00 N. Vayatis Lecture #2 Chapter 1 - Optimality in statistical learning

Slides
Tuesday October 20
11:00-13:00 TA Exercise session #1 Optimal elements and excess risk bounds

Sheet
Tuesday November 3
11:00-13:00 N. Vayatis Lecture #3 Chapter 2 - Mathematical foundations (I)
Probabilistic inequalities, complexity measures
Slides
Friday November 6
14:00-16:00
TA
!!Amphi 1Z18!!
Exercise session #2 Inequalities, Rademacher complexity, VC dimension

Sheet
Tuesday November 10
11:00-13:00
Partial exam - Mandatory
No documents allowed


Tuesday November 17
11:00-13:00 N. Vayatis Lecture #4 Chapter 2 - Mathematical foundations (II)
Regularization and stability
Slides
Tuesday November 24
11:00-13:00 N. Vayatis Lecture #5 Chapter 3 - Consistency of Machine Learning methods (I)
Boosting, SVM, Neural networks
Slides
Tuesday December 1 11:00-13:00 TA
Exercise session #3 Consistency and convergence bounds

Sheet
Tuesday December 8
11:00-13:00 N. Vayatis Lecture #6 Chapter 3 - Consistency of Machine Learning methods (II)
Bagging, Random Forests
Slides
Tuesday December 15 11:00-13:00 TA
Exercise session #4 Course wrap-up

Sheet
Tuesday January 5 (Tentative)
11:00-13:00
Final exam - Mandatory
Documents allowed




***Office hours:
Tuesdays 1pm/2pm


***Evaluation:
Partial exam on November 10 (11am-1pm) - MANDATORY
Final exam on January 5 (11am-1pm) - MANDATORY

***Grading:
Course grade = max(final ; (partial+final)/2)

 *************

Contents

Chapter 1 - Optimality in statistical learning:
    From information theory to statistical learning
    The probabilistic view on numerical data,
   
Optimality and the bias-variance dilemma
Chapter 2 - The mathematical foundations of statistical learning:
    Risk minimization
    Concentration inequalities
    Complexity measures
    (Explicit) Regularization
    (Stability)
Chapter 3 - Theory: Consistency theorems and error bounds of learning algorithms

    Part 1 - SVM, Neural Networks, Boosting
    Part 2 - Bagging, Random Forests
(Chapter 4 - Advanced topics)

Some lecture notes

Unofficial (and confidential!) lecture notes will be distributed after Lecture #3
Disclaimer: this document was offered by a former student, some mistakes may be found in the document.


Past exams


Final exam 2018 / Partial exam 2018  / Final exam 2017 / Partial exam 2017  / Final exam 2016 / Partial exam 2016


References