M1 Math ENS Cachan - Apprentissage statistique

DM. Implémentation de quelques algorithmes de classification.
TD9. Maximum Likelihood.
TD8. Data-Dependant Partitioning.
TD7. Applications of Stone's Theorem.
TD6. Consistency of Voting Classifiers.
TD5. The Nearest Neighbor Rule.
TD4. Online-to-Batch & Probabilistic Framework.
TD3. The Perceptron Algorithm and Bregman Divergence.
TD2. Online Learning and Game Theory.
TD1. Concentration inequalities.

MPRI 1.24 - Probabilistic aspects of computer science

TD6. Reachability Objectives in MDP & Probabilistic Automata.
TD5. MDP: Modelling and finite-horizon objectives.
TD4. Continuous-time Markov chains.
TD3. Random walks and applications.
TD2. Markov chains in the long run.
TD1. Discrete-time Renewal Processes and First Markov Chains.

L3 Info ENS Cachan - Projet Programmation 2

Partie 3. Améliorations et Programmation Avancée.
Partie 2. Diversifications et Programmation Orienté Objet.
Partie 1. Un Tower Defense simplifié, Interface Graphique.
Introduction. Le Langage Scala.

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