Cours associés (10)
EE-806: Multi Agent Reinforcement Learning
The goal of the summer school are providing a rigorous introduction to the foundations of MARL and highlight the challenges that arise in the modern research directions in this area.
ENG-704: EECS Seminar: Advanced Topics in Machine Learning
Students learn about advanced topics in machine learning, artificial intelligence, optimization, and data science. Students also learn to interact with scientific work, analyze and understand strength
COM-406: Foundations of Data Science
We discuss a set of topics that are important for the understanding of modern data science but that are typically not taught in an introductory ML course. In particular we discuss fundamental ideas an
CS-456: Deep reinforcement learning
This course provides an overview and introduces modern methods for reinforcement learning (RL.) The course starts with the fundamentals of RL, such as Q-learning, and delves into commonly used approac
EE-608: Deep Learning For Natural Language Processing
The Deep Learning for NLP course provides an overview of neural network based methods applied to text. The focus is on models particularly suited to the properties of human language, such as categori
ENG-466: Distributed intelligent systems
The goal of this course is to provide methods and tools for modeling distributed intelligent systems as well as designing and optimizing coordination strategies. The course is a well-balanced mixture
CS-430: Intelligent agents
Software agents are widely used to control physical, economic and financial processes. The course presents practical methods for implementing software agents and multi-agent systems, supported by prog
CH-457: AI for chemistry
The AI for Chemistry course will focus on teaching students how to use machine learning algorithms and techniques to analyze and make predictions about chemical data. The course will cover topics such
MGT-492: Data science and machine learning I
This class provides a hands-on introduction to data science and machine learning topics, exploring areas such as data acquisition and cleaning, regression, classification, clustering, neural networks,
CS-605: Computational and statistical learning theory
Statistical learning theory for supervised learning and generalization in PAC and online models (VC theory, MDL/SRM, covering numbers, Radamacher Averages, boosting, compression, stability and connect

Graph Chatbot

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.