Learning, Classification, and Compression

Dr. Erwin Riegler

Offered in:



Basic Information:

Lecture:Wednesday, 09:15-11:00, HG D 3.2, live broadcast on the ETH video portal. The first lecture takes place on Wednesday 23 Feb. 2022, 09:15-11:00.
Discussion session:  Wednesday, 11:15-12:00, HG D 3.2, live broadcast on the ETH video portal. The first discussion session takes place on Wednesday 02 Mar. 2022, 11:15-12:00.
Office hours: Wednesday, 15:15-16:15 via Zoom. The first office hour takes place on Wednesday 02 Mar. 2022, 15:15-16:15.
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).
Lecture Recording:The recordings of the lecture + discussion session can be found at this page (access credentials are the same as for the lecture/exercise notes).
Instructor: Dr. Erwin Riegler
Teaching assistants: Hongruyu Chen, Stefan Stojanovic
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.



News

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:



Prerequisites

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



Lecture Notes:



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



Corrections:



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. All the problem sets will be discussed in the discussion session, and the solutions will be uploaded afterwards.

Problems Solutions
Set 1 Solutions to Set 1
Set 2 Solutions to Set 2
Set 3 Solutions to Set 3
Set 4 Solutions to Set 4
Set 5 Solutions to Set 5
Set 6 Solutions to Set 6
Set 7 Solutions to Set 7
Set 8 Solutions to Set 8
Set 9 Solutions to Set 9
Set 10 Handout for Set 10 Solutions to Set 10
Set 11 Solutions to Set 11
Set 12


Previous years' exams and solutions

Summer Exam 2021: Problems Solutions Handout