Theory of Machine Learning

Instructor: 
Time I: 
Friday,
10:15am to 11:45am
Venue I: 
HU Berlin, Spandauer Str. 1, room 21b
Description: 

In the seminar, the theoretical foundations of machine learning will be discussed. Topic include probably almost correct learning, VC dimension, risk minimization, boosting, model selection, stochastic gradient descent, support vector machines, kernel methods, and neural networks. After an introduction to the general topic of machine learning, students will present a chapter in the book “Understanding machine learning” by Shalev-Shwartz and Ben-David (Cambridge Universit Press) and hand in a short summary of the key findings. Participation in the discussions is expected.

Literature:
“Understanding machine learning” by Shalev-Shwartz and Ben-David (Cambridge Universit Press)

Exam:
Presentation and portfolio (30,000 characters). The portfolio examination consists of a research project in which the students show their learning progress.

Credits: 
9.00
Semester: 
Fall 2019
Affiliation: 
Humboldt-Universität zu Berlin