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

Literature:

• 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)