- Intermediate delivery: March 19th (you provide a first solution for an early feedback)
- 10' videos due Sunday April 11th at last (please send us a link valid up to April 15th at least); the video should be inspired by the report writing guidelines
- Defenses: Wednesday April 14th pm
(we use BBB of "portail des études" by default and Zoom in case of a technical problem~~in presence, salle Condorcet~~online, due to the latest COVID measures

Schedule:**14h00**Simon Raul, Gaspard Thevenon, Idriss Gouigah, Ugo Insalaco, Martin Petitjean*Automatiser la détection d’apnée du sommeil***14h20**Guilhem Niot, Alexandre Variengien, Elias Suvanto, Timothé Picavet et Yann Aguettaz*Detecting Sleep Apnea from raw physiological signals***14h40**Sasha Darmon, Pierre Marrec, Adrien Pontlevy, Bastien Girault, Malory Marin*Interpreting neural networks predictions for multi-label classification of music catalogs***15h00**Yoann Poupart*Regional climate forecast***15h20**Julien Moreau et Antoine Lavandier*ML Project: detecting Sleep Apnea from raw physiological signals***15h40**Max Dedieu, Suban Suresh, Mickael Daveau, Manon Schieber, Victor Espuna, Timothée Galtier*CFM: stock trading: prediction of auction volume***16h00**Morisset Lucas, Amir Nikabadi, Emilie Vidal, Israel Campero Jurado*Regional Climate Forecast***16h20**Aymane S'Guiar, Anas Zilali, and Léo Gonçalves*Reconstruction of Liquet Asset Performance***16h40**Mattéo Clémot, Maël Feurgard, Ataollah Kamal, Emile Touileb and Théo Boury*Detecting Sleep Apnea from raw physiological signals*

Zoom link for the lectures by Yohann de Castro (19.01, 02.02, 23.02, 9.03 and 23.03)

backup discord server: please create an account in case of a problem!

Lectures: Tuesday 10:15-12:15

Hands-on sessions: Thursday 15:45-17:45

Homework 1

Homework 2 due March 26th: please send to Yohann de Castro

Homework 3 due April 9th: please send to Aurélien Garivier

Yohann de Castro, Aurélien Garivier

The aim of this Master 1 Informatique Fondamentale course is to introduce the basic theory and algorithms of Machine Learning. Topics to be taught (may be modified) ~20h of lectures + 20h of lab sessions.

- General introduction to Machine Learning: learning settings, curse of dimensionality, overfitting/underfitting, etc.
- Overview of Supervised Learning Theory: True risk versus empirical risk, loss functions, regularization, bias/variance trade-off, complexity measures, generalization bounds.
- Linear/Logistic/Polynomial Regression: batch/stochastic gradient descent, closed-form solution.
- Sparsity in Convex Optimization.
- Support Vector Machines: large margin, primal problem, dual problem, kernelization, etc.
- Neural Networks, Deep Learning.
- Theory of boosting: Ensemble methods, Adaboost, theoretical guarantees.
- Non-parametric Methods (K-Nearest-Neighbors)
- Domain Adaptation
- Metric Learning
- Optimal Transport

- Slides: Introduction, Clustering, Dimensionality reduction, Introduction to supervised learning (version 2021), CART algorithm, ERM, perceptron, convexification of risk, Robert Schapire's slides on Boosting, Structural Risk Minimization and SVM, deep learning, reinforcement learning
- Some videos of the lectures: Clustering, Introduction to supervised learning, ERM, perceptron, convexification of risk, Structural Risk Minimization, SVM and kernels
- Class notes (written by the students after each lecture).
- Exercises for Hands-on Sessions: correction of TD 1, correction of TP1, correction of TD 2, correction of TD 3, correction of TD 4, correction of TD 5, correction of TD 6, correction of TD 7 (corrections by Clément Lalanne)
- Notebooks for the hands-on sessions: text clustering, dimensionality reduction for MNIST classification (correction), Bias-variance tradeoff for k-nearest neighbors (correction), Fairness and classification on the adult data set, Kernel SVM for time series, Deep Learning, Reinforcemnet Learning: retail store management (julia version with a few more things)
- Projects on challengedata, Data for the Windmill Detection Project

Basic knowledge of probability theory, linear algebra and analysis over the reals

50% final exam, 50% project and in-class exercises.

- Statistical Learning Theory, V. Vapnik, Wiley, 1998
- Machine Learning, Tom Mitchell, MacGraw Hill, 1997
- Pattern Recognition and Machine Learning, M. Bishop, 2013
- Convex Optimization, Stephen Boyd & Lieven Vandenberghe, Cambridge University Press, 2012.
- On-line Machine Learning courses: https://www.coursera.org/