Fiches de cours

EECS Seminar: Advanced Topics in Machine Learning


Lecturer(s) :

Cevher Volkan
Faltings Boi
Flammarion Nicolas Henri Bernard
Frossard Pascal
Jaggi Martin
West Robert




Every year


Next time: Spring 2021


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 strengths and weaknesses of scientific arguments of both theoretical and experimental results.


List of general technical topics:

This course is held as an advanced seminar, and will familiarize students with recent developments in machine learning and AI in particular, and with the analysis and presentation of scientific work in general. Original research articles have to be presented and critically reviewed. The students will learn how to structure a scientific presentation in English. An important goal of the seminar presentation is to summarize the essential ideas of a research paper in sufficient depth while omitting details which are not essential for the understanding of the work, as well as to identify strengths and weaknesses of the paper at hand, that is to demonstrate critical interaction with the presented material of both their own paper but also their peers. The learned presentation and communication skills are beneficial for future presentations both in the industrial as well as scientific environment. 


Machine Learning, Optimization, Deep Learning, Artificial Intelligence.

Learning Prerequisites

Required courses

EE-556 Mathematics of Data,  CS-433: Machine Learning, CS330: Artificial Intelligence.

Learning Outcomes

By the end of the course, the student must be able to:

Assessment methods

Oral examination.

In the programs

Reference week

      Exercise, TP
      Project, other


  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German