Teaching
M1 Math ENS Cachan - Apprentissage statistique
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DM. Implémentation de quelques algorithmes de classification. |
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TD9. Maximum Likelihood. |
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TD8. Data-Dependant Partitioning. |
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TD7. Applications of Stone's Theorem. |
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TD6. Consistency of Voting Classifiers. |
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TD5. The Nearest Neighbor Rule. |
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TD4. Online-to-Batch & Probabilistic Framework. |
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TD3. The Perceptron Algorithm and Bregman Divergence. |
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TD2. Online Learning and Game Theory. |
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TD1. Concentration inequalities. |
MPRI 1.24 - Probabilistic aspects of computer science
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TD6. Reachability Objectives in MDP & Probabilistic Automata. |
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TD5. MDP: Modelling and finite-horizon objectives. |
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TD4. Continuous-time Markov chains. |
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TD3. Random walks and applications. |
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TD2. Markov chains in the long run. |
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TD1. Discrete-time Renewal Processes and First Markov Chains. |
L3 Info ENS Cachan - Projet Programmation 2
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Partie 3. Améliorations et Programmation Avancée. |
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Partie 2. Diversifications et Programmation Orienté Objet. |
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Partie 1. Un Tower Defense simplifié, Interface Graphique. |
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Introduction. Le Langage Scala. |