The class will be taught
in French or English, depending on attendance (all slides and class
notes are in English).
Given the sanitary situations, we will use an online "flipped classroom" methodology. For every lecture, sections of the book in preparation (check regularly for latest versions) will be highlighted. Students are expected to study the material *before* Friday morning. The friday morning online session will be divided in three groups (each group with a third of students) and students will have the opportunity to ask questions after the lecturer provides a quick overview of the material. Each student has to ask at least one question. Practical sessions will be done at home.
Please send the practical sessions (one jupyter notebook .ipynb with cells containing either text or runnable code) to email@example.com with the subject [PSn] with n being the number of the practical session (no acknowledgements will be sent back).
|LC||15 January||Introduction to supervised learning (loss, risk, over-fitting and capacity control + cross-validation, Bayes predictor for classification and regression||1.2.1,
2.1, 2.2, 2.3, 2.4
|FB||22 January||Least-squares regression (all aspects, from linear
algebra to statistical guarantees and L2 regularization +
Practical session 1, due February 12, 2021 (mnist_digits.mat)
|3.1, 3.2, 3.3, 3.4, 3.5, 3.6
|LC||29 January||Statistical ML without optimization (learning theory, from finite number of hypothesis to Rademacher / covering numbers)||4.1.1, 4.1.2, 4.2, 4.3, 4.4.(1-3), 4.5.(1-4)
|FB||5 February||Convex optimization (gradient descent + nonsmooth +
Practical session 2, due March 5, 2021
|5.1, 5.2.1, 5.2.2, 5.2.3, 5.3, 5.4 (not 5.4.1 and 5.4.2)
|LC||12 February||Local averaging techniques (K-nearest neighbor,
Nadaraya-Watson regression: algorithms + statistical analysis)
Practical session 3, due March 12, 2021
|6 (all sections except the diamond ones)
|FB||19 February||Kernels (positive-definite kernels and reproducing kernel Hilbert spaces)||7 (all sections except the diamond ones)|
||Model selection (feature selection, L1 regularization and high-dimensional inference + practical session)||8
|FB||12 March||Neural networks (from one-hidden layer to deep networks + practical session)||9
|LC||19 March||Special topics
Evaluation: practical sessions to do at home + written take-home exam