Courses 2020-2021

Would you like to focus your studies on Intelligent Systems related fields? EPFL offers a wide range of study courses.

Update 2022 coming soon

EPFL offers numerous courses in relation to Intelligent Systems, which covers various fields and disciplines, from mathematics and data science to robotics and computer vision.

Please note this list is not exhaustive. For more information, please contact: [email protected]

Bachelor

Course	Lecturer(s)	Field	P/T	Language	Online
HUM-388 – Personalized health: biomedical & social challenge	Chiapperino Luca	Biomedical	Both	FR/EN	N/A
CS-330 – Artificial intelligence	Faltings Boi	Artificial intelligence	Theory	FR	N/A
BIO-322 – Introduction to machine learning for bioengineers	Brea Johanni Michael	Machine Learning	Both	EN	N/A
CS-233(b) – Introduction to machine learning (BA4)	Pascal Fua Okan Altingövde Jan Bednarík Shuxuan Guo Sena Kiciroglu Weizhe Liu Krishna Kanth Nakka Edoardo Remelli Vidit Vidit	Machine Learning	N/A	EN	N/A
EE-311 – Apprentissage et intelligence artificielle	Fleuret François Liebling Michael Stefan Daniel	Machine Learning	Both	FR	N/A
MGT-302 – Data driven business analytics	Etesami Seyed Jalal Kiyavash Negar Malamud Semyon	Data Analysis	N/A	EN	N/A
CIVIL-226 – Introduction to machine learning for engineers	Alahi Alexandre	Machine Learning	Both	EN	N/A
ENG-209 – Data science pour ingénieurs avec Python	Eric Pierre Bouillet Jean-Philippe Pellet Olivier Verscheure Tao Sun	Data Science	N/A	FR	N/A
MATH-352 -Causal thinking	Stensrud Mats Julius	Math	N/A	EN	N/A

Master

Course	Lecturer(s)	Field	P/T	Language	Online
MGT-418 – Convex optimization	Daniel Kuhn	Mathematics	Both	EN	N/A
MATH-403 – Low-rank approximation techniques	Daniel Kressner	Mathematics	Theory	EN	N/A
MATH-412 – Statistical machine learning	Obozinski Guillaume Romain	Mathematics	Both	EN	N/A
MICRO-455 – Applied Machine Learning	Aude Billard	Machine Learning	TBD	EN	N/A
MICRO-401 – Machine Learning Programming	Aude Billard	Machine Learning	TBD	EN	N/A
CS-456 – Artificial neural networks	Gerstner Wulfram	Neural Networks	Theory	EN	N/A
CS-430- Intelligent agents	Faltings Boi	Intelligent Agents	Both	EN	N/A
CS-439 – Optimization for machine learning	Flammarion Nicolas Henri Bernard Jaggi Martin	Machine Learning	Both	EN	N/A
(ADA) CS-401 – Applied Data Analysis	Robert West	Data Analysis	Both	EN	Hybrid
EE-556 – Mathematics of data: from theory to computation	Cevher Volkan	Statististical Analysis	Both	EN	N/A
CS-526 – Learning theory	Macris Nicolas Urbanke Rüdiger	Machine Learning	Both	EN	N/A
EE-559 – Deep Learning	François Fleuret	Deep Learning	Both	EN	N/A
MICRO – 570 Advanced machine learning	Aude Billard Bernardo Fichera Lukas Huber Thomas Michaelsen Pethick Athanasios Polydoros	Machine Learning	N/A	EN	N/A
CS-413 –Computational photography	Süsstrunk Sabine	Computational photography	Both	EN	N/A
MATH-448 – Statistical analysis of network data	Olhede Sofia Charlotta	Statistics	Theory	EN	N/A
MATH-413 -Statistics for data science	Olhede Sofia Charlotta	Statistics	Theory	EN	N/A
MGT-448 – Statistical inference and machine learning	Negar Kiyavash Fateme Jamshidi Ehsan Mokhtarian Ehsan Mokhtarian Paul Thierry Yves Rolland	Statistics/Machine Learning	N/A	EN	N/A
MGT-416 – Network analytics	Negar Kiyavash Seyed Jalal Etesami René Yves Glogg	Network analysis	Theory	EN	On site optional
CIVIL-459 – Deep learning for autonomous vehicles	Alahi Alexandre	Deep learning	Both	EN	N/A
MATH-412 – Statistical machine learning	Obozinski Guillaume Romain	Statistics/ Machine Learning	Both	EN	N/A
CS 723 – Topics on ML Systems	Babak Falsafi Anne-Marie Kermarrec Martin Jaggi	Machine Learning	Theory	EN	Online
DH-411 – Design research for digital innovation	Henchoz Nicolas	Design	Both	EN	N/A
EE-451- Image analysis and pattern recognition	Thiran Jean-Philippe	Image analysis	Both	EN	N/A
CS-433 – Machine Learning	Nicolas Flammarion Martin Jaggi	Machine Learning	Both	EN	Online
CIVIL-426 – Machine learning for predictive maintenance applications	Fink Olga	Machine Learning	N/A	EN	Onsite

Doctoral courses and continued education

Course	Lecturer(s)	Field	P/T	Language	Online
PHYS-754- Lecture series on scientific machine learning	Carleo Giuseppe Ceriotti Michele Corminboeuf Anne-Clémence De Los Rios Paolo Mathis Alexander Wyart Matthieu Zdeborová Lenka	Machine learning	Theory	EN	PHH331
EE-608 – Deep Learning For Natural Language Processing	Henderson James	NPL	N/A	EN	N/A
EE-618 – Theory and Methods for Reinforcement Learning	Cevher Volkan	Reinforcement Learning	N/A	EN	N/A
EE-613 – Machine Learning for Engineers	Calinon Sylvain Fleuret François Odobez Jean-Marc	Machine Learning	N/A	EN	N/A
CS-723 – Topics in Machine Learning Systems	Falsafi Babak Jaggi Martin Kermarrec Anne-Marie	Machine Learning	N/A	EN	Online
EECS Seminar: Advanced Topics in Machine Learning ENG-704	Cevher Volkan Faltings Boi Flammarion Nicolas Henri Bernard Frossard Pascal Jaggi Martin West Robert	Machine Learning	Theory	EN	Online
MGT-618 – Computational Research Methods for Social Sciences	Kenneth A. Younge	Data analysis	Assignments and Project	EN	N/A
PHYS-754-Lecture series on scientific machine learning	Various lecturers Zdeborová Lenka	Machine Learning	Project	EN	N/A

EDRS – Robotics, Control, and Intelligent Systems 2020-21 (All courses)

Executive Certificate (CAS)

The Executive Certificate (CAS) in Data Science & Management is a unique program for executives, developed by HEC Lausanne and EPFL. The program combines management and technology perspectives and demystifies Big Data, Data Science, Artificial Intelligence and Machine Learning. Participants develop the required skills to drive the transformation towards a Data-Driven Enterprise and create sustainable business value from data.

The program comprises 4 modules that can also be taken as individual shorts programs (4×4 days) that address the strategic, organizational and technical challenges in data-driven organizations. To get more information about this short programs please check the program tab.

More info