Course devoted to machine learning topics for signal processing applications, with a specific focus on inverse problems. The lecture is in the continuity of the following courses from the Data Science Master's degree: Signal processing (M1 DS), Numerical Analysis and Optimization (M1 DS), Models for Machine Learning (M1DS), Bayesian Machine Learning (M2 DS).
Pre-requisites: Signal processing course from the M1 Data science study track (or equivalent), Fourier analysis, wavelet transform, filtering, notions in convex optimization (Lagrangian function)
Keywords: signal processing, image processing, inverse problems, Bayesian inference, (convex) optimization, PnP algorithms, MCMC algorithms.
- Teacher: Pierre Chainais
- Teacher: Pierre Antoine Thouvenin