Cours

CIVIL-459: Deep learning for autonomous vehicles

Résumé

Deep Learning (DL) is the subset of Machine learning reshaping the future of transportation and mobility. In this class, we will show how DL can be used to teach autonomous vehicles to detect objects, make predictions, and make decisions. (Fun fact: this summary is powered by DL)

Séances de cours

Enseignant·e

Alexandre Massoud Alahi

Alexandre Alahi is currently an Assistant Professor at EPFL. He spent five years at Stanford University as a Post-doc and Research Scientist after obtaining his Ph.D. from EPFL. His research enables machines to perceive the world and make decisions in the context of transportation problems and smart environments. He has worked on the theoretical challenges and practical applications of socially-aware Artificial Intelligence, i.e., systems equipped with perception and social intelligence. He was awarded the Swiss NSF early and advanced researcher grants for his work on predicting human social behavior. He won the CVPR Open Source Award (2012) for his work on Retina-inspired image descriptors, and the ICDSC Challenge Prize (2009) for his sparsity-driven algorithm that has tracked more than 100 million pedestrians to date. His research has been covered internationally by BBC, abc, PBS, Euronews, Wall street journal, and other national news outlets around the world. Alexandre has also co-founded multiple startups such as Visiosafe, and won several startup competitions. He was elected as one of the Top 20 Swiss Venture leaders in 2010.

Source officielle

https://edu.epfl.ch/coursebook/fr/deep-learning-for-autonomous-vehicles-CIVIL-459

Page Moodle

http://go.epfl.ch/CIVIL-459

Graph Chat

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Résumé

Séances de cours

Enseignant·e

Alexandre Massoud Alahi

Source officielle

https://edu.epfl.ch/coursebook/fr/deep-learning-for-autonomous-vehicles-CIVIL-459

Page Moodle

http://go.epfl.ch/CIVIL-459

Séances de ce cours (13)

Utilisation des grappes SCITAS

Couvre la configuration et l'utilisation des grappes informatiques à usage général à l'EPFL.

Véhicules autonomes: renseignement et perception

Explore l'intelligence, la perception et les applications de l'IA dans les véhicules autonomes, en mettant l'accent sur la pensée rationnelle et l'intelligence sociale.

Perception : Défis de classification des images

Couvre les défis de classification d'images, les concepts d'apprentissage automatique, la régression linéaire et l'approche voisine la plus proche dans les véhicules autonomes.

Perception : Approches fondées sur les données

Explore la perception dans l'apprentissage profond pour les véhicules autonomes, couvrant la classification d'image, les méthodes d'optimisation, et le rôle de la représentation dans l'apprentissage automatique.

Réseaux neuronaux convolutionnels

Introduit des réseaux neuronaux convolutionnels (RCN) pour les véhicules autonomes, couvrant l'architecture, les applications et les techniques de régularisation.

Afficher plus

Cours associés (32)

PHYS-467: Machine learning for physicists

Machine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practised. ...

EE-556: Mathematics of data: from theory to computation

This course provides an overview of key advances in continuous optimization and statistical analysis for machine learning. We review recent learning formulations and models as well as their guarantees

CS-433: Machine learning

Machine learning methods are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented. ...

CS-401: Applied data analysis

This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the data science world (pandas, scikit-learn, Spark, etc.) ...

EE-566: Adaptation and learning

In this course, students learn to design and master algorithms and core concepts related to inference and learning from data and the foundations of adaptation and learning theories with applications.

Afficher plus