INFO8010-1

Durée

30h Th, 5h Pr, 45h Proj.

Nombre de crédits

	Master en science des données, à finalité	5 crédits
	Master : ingénieur civil électricien, à finalité	5 crédits
	Master : ingénieur civil en informatique, à finalité	5 crédits
	Master : ingénieur civil en science des données, à finalité	5 crédits
	Master en sciences informatiques, à finalité	5 crédits

Enseignant

Gilles Louppe

Langue(s) de l'unité d'enseignement

Langue anglaise

Organisation et évaluation

Enseignement au deuxième quadrimestre

Horaire

Horaire en ligne

Unités d'enseignement prérequises et corequises

Les unités prérequises ou corequises sont présentées au sein de chaque programme

Contenus de l'unité d'enseignement

Latest developments in deep machine learning have enabled great and unprecedented advances in systems for visual recognition, speech and text understanding or autonomous intelligent agents. In this context, this course is a deep dive into the details of deep learning architectures, with a focus on learning end-to-end models for these tasks. Students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in the field. The course will also tour recent innovations in inference methods, including differential inference, adversarial training and Bayesian deep learning.
Topics to be covered (tentative and subject to change):

Fundamentals of machine learning
Multi-layer perceptron
Convolutional neural networks
Training neural networks
Recurrent neural networks
Unsupervised learning
Differentiable inference and generative models
Theory of deep learning
Adversarial training
Bayeisan deep learning

Acquis d'apprentissage (objectifs d'apprentissage) de l'unité d'enseignement

At the end of the course, the student will have acquired a solid and detailed understanding of the field of deep learning. He/she will have studied both well-established and novel algorithms (in theory and in practice), and will also have become familiar with some of the many open research questions and challenges of the field.

Savoirs et compétences prérequis

Programming experience. Probability and statistics.
Following "ELEN0062 Introduction to Machine learning" before taking this class is strongly recommend.

Activités d'apprentissage prévues et méthodes d'enseignement

Theoretical lectures
Reading assignment
Programming projects

Mode d'enseignement (présentiel ; enseignement à distance)

Lectures will taught face-to-face. Projects will be carried out remotely.

Lectures recommandées ou obligatoires et notes de cours

Slides will be made publicly available on GitHub during the semester.

Modalités d'évaluation et critères

Exam (50%)
Reading assignment (10%)
Programming projects (40%)

The reading assingment and the programming projects are mandatory for presenting the exam.

Deep learning