François Fleuret
Adjunct Professor
francois.fleuret@epfl.ch +41 21 693 40 88 https://fleuret.org/francois/
Citizenship : Swiss, French
Birth date : 10.01.1972
Biography
He is Full Professor in the department of Computer Science at the University of Geneva, and Adjunct Professor in the School of Engineering of the École Polytechnique Fédérale de Lausanne. 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, 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.
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 development and commercialization of deep learning solutions for engineering design.
His main research interest is machine learning, with a particular focus on computational aspects and small sample learning.
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Fields of expertise
Teaching & PhD
Teaching
Electrical and Electronics Engineering
PhD Programs
PhD Students
Courdier Evann Pierre Guy, Dimitriadis Nikolaos, Janbakhshi Parvaneh, Johari Seyed Mohammad Mahdi, Kabil Selen Hande, Katharopoulos Angelos, Marelli Francois Thierry M, Matoba Kyle Michael, Pagliardini Matteo, Pannatier Arnaud, Shajkofci Adrian, Sivaprasad Prabhu Teja, Srinivas Suraj, Vyas Apoorv,Past EPFL PhD Students
Ali Karim , Bagautdinov Timur , Baqué Pierre Bruno , Ben Shitrit Horesh Beny , Berclaz Jérôme , Canévet Olivier , Chavdarova Tatjana , Dubout Charles , González Serrano Germán , Jose Cijo , Lefakis Leonidas , Newling James Peter , Suditu Nicolae , Tulyakov Stepan ,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 course aims at providing an overview of existing processings and methods, at teaching how to design and train a deep neural network for a given task, and at providing the theoretical basis to go beyond the topics directly seen in the course.
It will touch on the following topics:
- What is deep learning, introduction to tensors.
- Basic machine-l
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.