Mathematical Introduction to Machine Learning

How do popular learning algorithms work? What guarantees are there that the learning was successful? To answer these questions we will discuss a number of different topics and draw on techniques from different fields, such as

  • convex optimisation
  • sample complexity
  • PAC learning
  • VC dimension

Participants should have basic knowledge in calculus, linear algebra and probability theory.

Times and Place:

  • Lecture: Tuesdays 14:15-15:45 in H13 and Thursdays 12:15-13:45 in N24 226
  • Exercise: Fridays 12:30-14:00 in H12


  • Understanding Machine Learning, Shai Shalev-Shwartz and Shai Ben-David
  • Foundations of Machine Learning, Mehryvar Mohri, Afshin Rostamizadeh and Ameet Talwakar

Henning Bruhn-Fujimoto (lecturer) & Felix Bock (teaching assistant)



First lecture:   Thursday, 17.10.2019
First exercise: Friday,      25.10.2019