The class will be taught
in French or English, depending on attendance (all slides and class
notes are in English).
Classes will be held in the room L367 (third floor, ENS, 24 rue Lhomond), Friday morning from 9am to 12pm when no practical sessions, and to 12.30pm when there are practical sessions. Class notes will be made available. Practical sessions will be held on laptops with Python 3 and Jupyter notebooks (please make sure to install it before January 17, and run this script).
notes / code
|FB||10 January||Introduction to supervised learning (loss, risk, over-fitting and capacity control + cross-validation, Bayes predictor for classification and regression||lecture1.pdf|
|LC||17 January||Least-squares regression (all aspects, from linear algebra to statistical guarantees and L2 regularization + practical session)||lecture2.pdf
|FB||24 January||Statistical ML without optimization (learning theory, from finite number of hypothesis to Rademacher / covering numbers)|
|FB||31 January||Local averaging techniques (K-nearest neighbor, Nadaraya-Watson regression: algorithms + statistical analysis + practical session)|
|LC||7 February||Empirical risk minimization (logistic regression, loss-based supervised learning, probabilistic interpretation through maximum likelihood)|
|LC||14 February||Convex optimization (gradient descent + nonsmooth + stochastic versions + practical session (logistic regression))|
|FB||28 February||Model selection (feature selection, L1 regularization and high-dimensional inference + practical session)|
|FB||6 March||Kernels (positive-definite kernels and reproducing kernel Hilbert spaces + practical session)|
|LC||13 March||Neural networks (from one-hidden layer to deep networks + practical session)|
|LC||20 March||Unsupervised learning (K-means and PCA (potentially with kernels) + mixture models (potentially EM) + practical session)|
Evaluation: practical sessions to finish at home + written in-class exam