Graduate School and Research Center in Digital Sciences

Benoit HUET

Benoit HUET
Benoit HUET
Eurecom - Data Science 
Assistant-Professor
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04 93 00 82 00
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Teaching

  • His current teaching activity includes the following courses : Multimedia Technologies, Advanced Topic in Multimedia and Intelligent Systems.
  • His teaching experience also includes the following topics : Neural Networks, Computer Vision and Pattern Recognition

My courses

  • ADST / Fall 2018 - Advanced Data Science Topics

    In this course, we will discuss contemporary and state of the art research problems in Data Science. The content of the course will change from year to year and will reflect the current research interests of the EURECOM faculty. The course is organised partly in Seminars/Case Studies sessions supported by industrials and researchers working in the field and a Mini Scientific Conference where each student will research and present a topic from the wide range of advanced data science topics.

    Teaching and Learning Methods : academic and industrial seminars, case studies in small group, written and oral presentation.

    Course Policies : Attendance to all sessions is mandatory.

  • DeepLearning / Spring 2019 - Deep Learning

    Deep Learning is a new approach in Machine Learning which allows to build models that have shown superior performance fora wide range of applications, in particular Computer Vision and Natural Language Processing. Thanks to the joint availability of large data corpus and affordable processing power, Deep Learning has revived the old field  of Artificial Neural Networks and provoked the "Renaissance" of AI (Artificial Intelligence). The objective of this course is to provide an overview of the field of Deep Learning, starting from simple Neural Network architectures and pursuing with contemporary and state of the art practices and models. The course is organized as a combination of  lectures where the theory is exposed and discussed, and hands-on sessions (labs) where experimentations are performed to practice with the theoretical concepts.

    Teaching and Learning Methods : The course is composed of a combination of lectures and labs.

    Course Policies : Attendance to all sessions is mandatory.

  • MALIS / Fall 2018 - Machine Learning and Intelligent System

    The objective of this course is to give students a solid background in Machine Learning (ML) techniques. ML techniques are used to build efficient models for problems for which an optimal solution is unknown. This course will introduce the basic theories of Machine Learning, together with the most common families of classifiers and predictors. It will identify the basic ideas underlying the mechanism of learning, and will specify the practical problems that are encountered when applying these techniques, optimization, overfitting, validation, together with possible solutions to manage those difficulties.

    Teaching and Learning Methods : Lectures and Lab sessions (groups of 1 or 2 students)

    Course Policies : Attendance to Lab sessions is mandatory.

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Distinctions

  • He and his co-author (M. Paleari) received the Best Student Paper Award for their work on "Toward emotion indexing of multimedia excerpts" published at the International Workshop on Content Based Multimedia Indexing, 2008.