Learning, Classification, and Compression

Dr. Erwin Riegler

Offered in:

Basic Information:

Lecture:Wednesday, 10:15-12:00, HG D 3.2. The first lecture takes place on Wednesday 22 Feb. 2023, 09:15-12:00.
Discussion session:  Wednesday, 09:15-10:00, HG D 3.2. The first discussion session takes place on Wednesday 01 Mar. 2023, 09:15-10:00.
Office hours: Friday, 10:15-11:00 via Zoom. The first office hour takes place on Friday 03 Mar. 2023, 10:15-11:00.
Zoom Links:The Zoom link for the office hours can be found at this page (access credentials are the same as for the lecture/exercise notes).
Instructor: Dr. Erwin Riegler
Teaching assistant: Alex Bühler
Lecture notes: Detailed lecture notes will be made available as we go along.
Prerequisites: This course is aimed at students with a solid background in measure theory and linear algebra and basic knowledge in functional analysis.
Credits: 4 ECTS credits.
Course structure: The class will be taught in English. There will be a written exam in English of duration 180 minutes.

Course Information:

The focus of the course is aligned to a theoretical approach of learning theory and classification and an introduction to lossy and lossless compression for general sets and measures. We will mainly focus on a probabilistic approach, where an underlying distribution must be learned/compressed. The concepts acquired in the course are of broad and general interest in data sciences.

After attending this lecture and participating in the exercise sessions, students will have acquired a working knowledge of learning theory, classification, and compression.


We will post important announcements, links, and other information here in the course of the semester, so please check back often!

Content of the Course:


This course is aimed at students with a solid background in measure theory and linear algebra and basic knowledge in functional analysis.

Lecture Notes and problems+solutions:

Tracking of how far we've come in the lecture:

Problem sets and solutions

There will be several problem sets for this course, which will help you better understand the lectures and prepare you for the exam (See download link Problems + Solutions above). The following problem will be discussed in the discussion session:

Discussion Session:Problem:
01.03.2023Problem 1
08.03.2023Problem 2
15.03.2023Problem 3
22.03.2023Problem 4
29.03.2023Problem 5
05.04.2023Problem 6
19.04.2023Problem 7
26.04.2023Problem 8
03.05.2023Problem 9
10.05.2023Problem 10
17.05.2023Problem 11
24.05.2023Problem 12

Parts of the notes relevant for the exam:

Note that you are not required to learn proofs by heart or to recite key steps in proofs. You are, however, expected to understand the main ideas/techniques/concepts used in the proofs in the relevant material listed above.

Previous years' exams and solutions

Summer Exam 2021: Problems Solutions Handout
Summer Exam 2022: Problems Solutions Handout