Learning, Classification, and Compression

Offered in:

Data Science Master: Wählbare Kernfächer
Doktorat Departement Informationstechnologie und Elektrotechnik: Lehrangebot Doktorat und Postdoktorat
Elektrotechnik und Informationstechnologie Master: Vertiefungsfächer
Elektrotechnik und Informationstechnologie Master: Empfohlene Fächer
Mathematik Bachelor: Auswahl: Weitere Gebiete
Mathematik Master: Auswahl: Weitere Gebiete
Physik Master: Allgemeine Wahlfächer
Statistik Master: Statistische und mathematische Fächer
Statistik Master: Fachbezogene Wahlfächer

Basic Information:

Lecture:	Monday, 10:15-12:00, ML F38. The first lecture takes place on TBA.
Discussion session:	Monday, 09:15-10:00, ML F38. The first discussion session takes place on TBA.
Office hours:	Friday, 14:15-15:00 via Zoom. The first office hour takes place on TBA.
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
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 an oral exam of duration 30 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:

Learning Theory:

Framework of Learning
Hypothesis Spaces and Target Functions
Reproducing Kernel Hilbert Spaces
Bias-Variance Tradeoff
Estimation of Sample and Approximation Error

Classification:

Binary Classifier
Support Vector Machines (separable case)
Support Vector Machines (nonseparable case)
Kernel Trick

Lossy and Lossless Compression:

Basics of Compression
Compressed Sensing for General Sets and Measures
Quantization and Rate Distortion Theory for General Sets and Measures

Prerequisites

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