EE-735 / 4 credits

Teacher: Cevher Volkan

Language: English


Every 2 years


This course provides an overview of recent developments in online learning, game theory, and variational inequalities and their point of intersection with a focus on algorithmic development. The primary approach is to lay out the different problem classes and their associated optimal rates.



The students are expected to build/present Lectures 6 and onwards at the end of the semester for grade.


Online learning, bandits, game theory, variational inequalities, adaptivity, monotone operators, regret, lower-bounds

Learning Prerequisites

Recommended courses

EE-556 Mathematics of Data is recommended.

Important concepts to start the course

Basic probability and linear algebra.

Learning Outcomes

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

  • Choose
  • Analyze algorithms
  • Explain regret
  • Produce a presentation
  • Theorize appropriate structures in optimization
  • Present concepts in game theory

Teaching methods

Lecture + active learning

Expected student activities

Build and present part of the material with the teaching team in form of a lecture.

In the programs

  • Number of places: 30
  • Exam form: Oral presentation (session free)
  • Subject examined: Online learning in games
  • Lecture: 28 Hour(s)
  • Practical work: 42 Hour(s)

Reference week