Introduction to Machine Learning (NYU Paris, Fall 2020)

Machine Learning is getting more and more important these days with applications ranging from autonomous driving to computer assisted medicine, including weather or financial forecasting. In this class we will study the mathematical foundations of the current machine learning algorithms.

We will cover the main models from both supervised learning including linear and non linear regression and classification (kernel methods, support vector machine, neural networks) and unsupervised learning (including clustering, gaussian mixtures, self organizing maps, principal and independent component analysis and non linear dimensionality reduction)

We will review basic concepts in probability and statistics. We will discuss Bayesian vs frequentist statistics and model/parameter inference, as well as sampling methods.

Finally, we will also discuss the important question of model assessment and selection.

The class will follow the structure 

1. Lectures (introduction of the new material that will be needed during the lab sessions and for the assignements)

2. Programming (lab) sessions, (you have the opportunity to apply what you have learned during the lecture, and you can ask all the questions you want to make sure you understand everything before the assignement)

3. Assignments (You are given a new problem and you are evaluated on your ability to use the course material to solve this new problem)

Schedule and Classroom

Lecture: Tuesday/Thursday, 12.30pm – 1.45pm, Room 401
Recitations: Thursday 2.15pm – 3.45pm. Room 401
Office hour: Tuesday 2.15pm – 3.45pm


Assignments policy

Except if explicitely stated otherwise, assignments are due at the beginning of each class.


Current (temporary) version of the notes:   Lecture notes as well as the list of sections for the Final

Practice (theory) Questions for each exam can be found by clicking on those exams below

Exam : 60% of the grade (30% Midterm (Material), 30% Final(Material))

Assignments : 30 % of the grade (Tentative schedule below)

 Final Project : 10 % of the grade (Tentative schedule below, List of suggestions, Poster guidelines)


The Github page for the class will be hosted at and will be used for the lab and the assignments. You can also click on each “Lab” in the schedule below and this will re-direct you to the github page.

Tentative schedule:

Legend: Lab sessions are in green, Homeworks are in red (right side of the table), dates related to the project are in orange.

Week # date Topic Assignements
Week 1 09/01, 09/03 General Intro + reminders on proba and inference. Part I,
    Part I : supervised Learning  
Week 2 09/08, 09/10 Linear and logistic regression, regularization and Compressed sensing
Linear Classification Part I, Part II, Note on the Bias-Variance trade-off
Lab 1, Solution Q1
Week 3 09/22, 09/24 Linear and logistic regression, Linear Classification (Part II)
Lab 2, Class Demo
Week 4 09/29, 10/01 Non Linear classification, Kernel methods, SVM, Parts I & 2
Lab 3
Week 5 10/06, 10/08 Neural Networks, Optimization, Stochastic Optimization, Deep learning, Part I
Lab 4 (Part I) / Solutions
Assign. 1, Readings
Week 6 10/13, 10/15 Lab 2: Non Linear regression and classification, Neural Nets
Lab 5 Zoom Slides
Project choice
    Part II : Unsupervised Learning
Week 7 10/20, 10/22 Clustering, Linear Latent variable models  Slides
Lab 6
Week 8 10/27, 10/29 Linear Latent variable models (Part II), PCA, ICA, GMM, EM algorithm,
Non linear LVM,  Part I Part II, Additional Note on MVN

Demos FA/PCA
Week 9 11/03, 11/05 Non Linear LVM  and Manifold Learning  Parts 1&2
Lab 7
Week 10 11/10, 11/12 Lab 3: Unsupervised Learning Assign. 3, Readings
Week 12 11/17, 11/19 Generalization, complexity and VC Theory
Week 13 11/24, 11/26 Probabilistic models, HMM, Bayesian Nets
Week 14 12/01, 12/03 Advanced topics,
Reinforcement Learning, Adversarial Learning, Part I
Assign. 4
Week 15 12/08, 12/10 Revisions
Week 16 12/15, 12/17 Project Presentations  


Lab Sessions and programming policy

The lab sessions will require you to do some programming. It is strongly recommended to use python as it is more flexible and will be useful to you when moving to pytorch later on for more advanced machine learning methods requiring GPU processing.

Downloading and getting started with Python.

Data sets can be downloaded on the following websites: