Programme MVA

The MVA Masters 2 programme runs over an academic year:

• end September to end February: coursework 
• beginning March to end August:research placement

The interdisciplinary classes are listed below:

• Classes on mathematics/vision (during term 1)

1. Optimisation and application methods in image processing by Profs Jean-François AUJOL / Mila NIKOLOVA
2. Introduction to digital imaging by Profs Yann GOUSSEAU /Julie DELON
3. Sub-pixel imaging by Prof Lionel MOISAN
4. Online image processing:partial differential equations and other fundamental algorithms, and their online publication by Jean-Michel MOREL
5. Parsimonious representations, estimation and wavelet compression by Prof Stéphane MALLAT
6. 3-D vision and reconstruction by Prof Renaud KERIVEN
7. Object reconstruction and artificial vision by Prof Jean PONCE      
8. Advanced Mathematical Methods in Computer Vision by Prof Nikos PARAGIOS

• Classes on Mathematics/Learning/Biological Signals (during term 1)

1. MCMC methods and applications by Profs Stéphane ALLASSONIERE / Eric MOULINES / Gersende FORT
2. Introduction to statistical learning by Prof Jean-Yves AUDIBERT
3. Probabilistic graphic models (Bayesian networks) by Prof Francis BACH
4. Learning by reinforcement by Prof Rémi MUNOS
5. Mathematical models for neuroscience by Prof Olivier FAUGERAS
6. Acquisition and digital processing of biomedical images (I) by Profs Nicolas AYACHE / Grégoire MALANDAIN
7. Dynamics, Control and Robotics by Profs Karine BEAUCHARD / Pierre ROUCHON

• Classes on Mathematics/Vision/Audio (during term 2)

1. Stochastic image analysis methods by Profs Agnès DESOLNEUX / Julie DELON
2. Compressed sensing by Prof Gabriel PEYRE
3. Deformable models in image and surface analysis by Profs Laurent COHEN / Gabriel PEYRE
4. Variational and statistical methods in video analysis by Profs François DIBOS / Georges KOEPFLER
5. Machine Learning for Computer Vision by Prof Lanosas KIKKINOS
6. Sound signal processing, time-frequency analysis by Prof Emmanuel BACRY
7. Audio-frequency signal analysis by Profs Gaël RICHARD / Yves GRENIER     
8. Satellite imaging by Profs Jean-Marie NICOLAS / Andres ALMANSA / Marine CAMPEDEL / Michel ROUX / Florence TUPIN

• Classes on Mathematics/Learning/Biological Signals (during term 2)

1. Advanced statistical learning by Prof Nicolas VAYATIS
2. Prediction learning and games by Prof Gabor LUGOSI
3. Kernel learning methods by Prof Jean-Philippe VERT
4. Analysing neuronal data and techniques by Prof Marie COTTREL     
5. Modelling in neuroscience – and elsewhere by Prof Jean-Pierre NADAL
6. Statistical Learning in Computational Biology by Prof Donald GEMAN
7. Information processing in biotechnology: statistical micro-array data analysis by Prof Bernard CHALMOND
8. Shaped geometry and space by Profs Joan GLAUNES / Alain TROUVE / Laurent YOUNES
9. Biomedical image analysis and simulation (II) by Profs Nicholas AYACHE / Hervé DELINGETTE / Xavier PENNEC
10. Functional brain imaging and brain-machine interface by Profs Théo PAPADOPOULO / Maureen CLERC / Bertrand THIRION