Cours
Du premier cycle au doctorat, l’EPFL propose de nombreux cours de Machine Learning.
Bachelor
- CIVIL-226 – Introduction to machine learning for engineers
- CS-233(a) – Introduction to machine learning (BA3)
- CS-233(b) – Introduction to machine learning (BA4)
- CS-330 – Artificial intelligenceCS-330 – Artificial Intelligence
- BIO-322 – Introduction to machine learning for bioengineers
- MATH-352 – Causal thinking
- ME-390 – Foundations of Artificial Intelligence
Master
- CIVIL-459 – Deep learning for autonomous vehicles
- CS-401 – Applied Data Analysis
- CS-430 – Intelligent Agents
- CS-433 – Machine Learning
- CS-439 – Optimization for Machine Learning
- CS-449 – Systems for data science
- CS-526 – Learning theory
- CS-503 – Visual intelligence : machines and minds
- COM-406 – Foundations of Data Science
- DH-406 – Machine Learning for the Digital Humanities
- EE-411 – Fundamentals of inference and learning
- EE-452 – Network machine learning
- EE-556 – Mathematics of data
- EE-559 – Deep Learning
- EE-566 – Adaptation and learning
- ENV-540 – Image processing for Earth observation
- MATH-403 – Low-rank approximation techniques
- MATH-412 – Statistical machine learning
- MATH-520 – Mathematics of machine learning
- MGT-418 – Convex optimization
- MICRO-401 – Machine Learning Programming
- MICRO-455 – Applied Machine Learning
- MICRO-570 – Advanced Machine Learning
Cours de doctorat et formation continue
- CS-612 – Topics in Natural Language Processing
- CS-723 – Topics in Machine Learning Systems
- EE-608 – Deep Learning For Natural Language Processing
- EE-613 – Machine learning for engineers
- EE-618 – Theory and Methods for Reinforcement Learning
- EE-735 – Online learning in games
- ENG-704 – EECS Seminar: Advanced Topics in Machine Learning
- EPFL Extension School – Applied Data Science: Machine Learning