François Fleuret

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Adjunct Professor

francois.fleuret@epfl.ch http://www.idiap.ch/~fleuret/

Citizenship : Swiss, French

Birth date : 10.01.1972

Biography
François Fleuret got a PhD in Mathematics from INRIA and the University of Paris VI in 2000, and an Habilitation degree in Mathematics from the University of Paris XIII in 2006.

He is the head of the Machine Learning group at the Idiap Research Institute, Switzerland, and Adjunct Professor in the School of Engineering of the École Polytechnique Fédérale de Lausanne, where he teaches machine learning. He has published more than 80 papers in peer-reviewed international conferences and journals. 

He is Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), serves as Area Chair for NeurIPS, AAAI, and ICCV, and in the program committee of many top-tier international conferences in machine learning and computer vision. He is member of the Electrical Engineering Doctoral Program Committee at EPFL, and was or is expert for multiple funding agencies (Swiss National Science Foundation, European Research Council, Austrian Science Fund, Netherlands Organization for Scientific Research, French National Research Agency, Research Council of the Academy of Finland, US National Science Foundation). 

He is the inventor of several patents in the field of machine learning, and co-founder of Neural Concept SA, a company specializing in the design and commercialization of AI learning-based solutions for engineering design. 

His main research interest is machine learning, with a particular focus on computational aspects and small sample learning. 

EPFL STI IEL LIDIAP
ELE 136 (Bâtiment ELE)
Station 14
CH-1015 Lausanne

EPFL > STI > IEL > LIDIAP

Web site: Web site: https://idiap.epfl.ch/

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Administrative data

Fields of expertise

Statistical machine learning, Deep learning, Pattern recognition

Publications

Infoscience publications

Teaching & PhD

Teaching

Electrical and Electronics Engineering

PhD Programs

Doctoral Program in Electrical Engineering

Doctoral program in computer and communication sciences

Courses

Fundamentals of machine learning

This course provides a general overview of machine learning, covering the main algorithms, theoretical formalisms and experimental protocols.

Deep learning

The objective of this course is to provide a complete introduction to deep machine learning. How to design a neural network, how to train it, and what are the modern techniques that specifically handle very large networks.

Machine Learning for Engineers

The objective of this course is to give an overview of machine learning techniques used for real-world applications, and to teach how to implement and use them in practice.