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 »).
1/ Introduction to statistical learning : supervised, non-supervised and reinforcement learning, general learning procedure, model evaluation, under and overfitting
2/ K-nearest neighbors and the « curse of the dimensionality »
3/ Regression in high dimensions, variable selection and model regularization (ridge, lasso)
4/ Stochastic gradient descent, mini-batch
5/ Neural networks: introduction, operator, datasets, training, examples, implementations
6/ K-means clustering
Main document for the theoretical presentations: (no distribution autoried without WRITTEN consent from the author): see your « teams » group.
Exercices, implementations: see « teams » group.