Statistical Learning, M1 Math 2024-2026

Instructor: Gabriel TURINICI

Preamble: this course is just but an introduction, in a limited amount of time, to Statistical and Machine learning. This will prepare for the next year’s courses (some of them on my www page cf. « Deep Learning » and « Reinforcement Learning »).

 

Course outline

1/ Examples and machine learning framework

2/ Useful theoretical objects: predictors, loss functions, bias, variance

3/ K-nearest neighbors (k-NN) and the « curse of the dimensionality »

4/ Linear and logistic models in high dimension, variable selection and model regularization (ridge, lasso)

5/ Stochastic Optimization Algorithms

6/ Naive Bayesian classification

7/ Neural networks : introduction, operator, datasets, training, examples, implementations

8/ K-means clustering

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Reference: 

Machine Learning Algorithms: From Classical Methods to Deep Neural Networks: Supervised, Unsupervised, and High-Dimensional

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Exercices, implementations, current course textbook (no distribution autorized without WRITTEN consent from the author): see « teams » group.

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