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