Notions et techniques abordées

Setting of the Learning Problem - Consistency of Learning Processes - Bounds on the Rate of Convergence of Learning Processes - Controlling the Generalization Ability of Learning Processes - Methods of Pattern Recognition - Methods of Function Estimation - Direct Methods in Statistical Learning Theory - The Vicinal Risk Minimization Principle and the Kernel Methods - Hidden Markov Models - Maximum likelihood for the HMM - The forward-backward algorithm - The Viterbi algorithm - Linear Dynamical Systems.