Teaching staff: F. d’Alché-Buc and E. Le Pennec with S. Gaiffas and Y. Ollivier
Machine Learning 1:
lecture 1 (02/10) : Introduction to Machine Learning, Probabilist framework for supervised learning, linear methods
lecture 2 (09/10): Kernel methods (à la Nadaraya-Watson) / validation and selection
lecture 3 (16/10): Aggregation and trees
lecture 4 (23/10): Representation and features
lecture 5 (27/10): SVM (SVR:exercice)
practical session 6 (7/11) : practical session
lecture 7 (14/11): RKHS point of view and regularization – Télécom ParisTech
lecture 8 (20/11): Kernel selection and learning, applications – Télécom ParisTech [ course + practical session]
Machine Learning 2:
lecture 9 (27/11): Deep learning I (Y. Ollivier, Télécom ParisTech)
lecture 10 (04/12): Deep learning II (Y. Ollivier, Polytechnique)
Exam (11/12) : Polytechnique
lecture 11 (19/12) : Unsupervised learning (F. d’Alché, Télécom ParisTech)
lecture 12 (8/01) : Semi-supervised learning (F. d’Alché, Polytechnique)
lecture 13/14 (15/01 and 22/01) : Collaborative filtering (S. Gaiffas, Polytechnique)
Practical session (29/01) : (E. Le Pennec, Polytechnique)