Fiches de cours 2017-2018


Seminar: Advanced Topics in Machine Learning


Lecturer(s) :

Cevher Volkan
Faltings Boi
Jaggi Martin
West Robert




Only this year


Next time: Spring 2018


This seminar introduces the participants to the current trends, problems, and methods in the area of machine learning, artificial intelligence and data science. Recent research papers are presented by the students and analyzed and discussed in plenary.


In this seminar, students learn about advanced topics in machine learning, artificial intelligence and data science. At the same time, students 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:

-              Recent trends in deep learning and representation learning

-              Scalable convex and non-convex optimization for machine learning

-              Distributed and parallel methods and systems for machine learning

-              Multi-Agent Learning, Machine Teaching and Adversarial Learning

-              Adaptive, single shot and zero-shot learning

-              Analysis and predictions in social networks, the web, Wikipedia

-              Network algorithms

-              Computational Social Science

-              Natural Language Processing


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.


Learning outcomes:

' Experience recent developments in machine learning methods and applications.

' Analyze and criticize scientific work

' Learn to synthesize arguments into convincing scientific presentations


Machine Learning,Deep Learning, Artificial Intelligence

Learning Prerequisites

Required courses

CS-433: Machine Learning
CS-330: Artificial Intelligenc



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