— Lecture 2 —

A brief overview of game theory

Lecturer: Michael Muehlebach (MPI-IS)
Time: (Zurich time)
Notes: Click here to download!
Recording: Click here to view! (only for ETH members)


The lecture will summarize key ideas in game theory. Game theory provides a means for modelling interactions between machine learning algorithms and their environment. We will revisit zero-sum games and von Neumann’s minimax theorem and introduce the concept of Nash equilibria. We will then discuss repeated games and adaptive decision-making algorithms (follow the leader, follow a random leader, multiplicative weights).

Recommended reading:

  • Karlin, A. R., & Peres, Y. (2017). Game theory, alive. American Mathematical Society. ISBN: 978-1-4704-1982-0. PDF version available online. [Chapter 2: Section 2.1–2.3; Chapter 18: Section 18.1–18.3]