VVZ API is not affiliated with ETH Zurich. Data might be outdated or incorrect. Please view the official ETHZ Vorlesungsverzeichnis for binding information.

401-2684-00L 6 Credits BSC D-MATH

Mathematics of Signals, Networks, and Learning

Lecturers & Examiners: Prof. Dr. Afonso Sousa Bandeira
VVZ CR n/a

Last Updated: 2026-06-03 00:37:49

Abstract

Introductory course to Mathematical aspects of Signal Processing, Network Theory, and Machine Learning. It showcases how different areas of Mathematics (including, but not limited to: Linear Algebra, Probability, Number Theory, Statistics, Combinatorics) interact and find applications in Data Science and related fields.

Objective

Introduction to Mathematical aspects of Signal Processing, Network Theory, and Machine Learning. This course also aims to showcase how different areas of Mathematics (including, but not limited to: Linear Algebra, Probability, Number Theory, Statistics, Combinatorics) interact and find applications in Data Science and related fields.

Content

Mathematical aspects of Supervised Learning, Unsupervised Learning, Sparsity, and Networks. This course is a Mathematical course, with Theorems and Proofs.

Resources

Lecture Notes

https://people.math.ethz.ch/~abandeira//MathofSNLnotes2025.pdf

General Information

Language
English
Levels
BSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 180 minutes
Aids
None
A bonus of up to 0.25 grade points can be achieved by the submission of a small number of homework solutions (more details to be announced in the course). (This is non-mandatory, and the maximum grade of 6 in the course unit can still be achieved by simply taking the final examination.)

Registration & Places

Max Places
100
Priority: Registration for the course unit is until 26.01.2026 only possible for the primary target group

Course Components

Type Title Time & Place Hours
lecture Mathematics of Signals, Networks, and Learning
  • Fri 13:15-16:00 (HG D 3.2)
3 h weekly
exercise Mathematics of Signals, Networks, and Learning
Groups are selected in myStudies.
  • Wed 16:15-18:00 (HG D 7.1)
  • Wed 16:15-18:00 (HG E 1.2)
  • Thu 16:15-18:00 (HG E 33.3)
  • Thu 16:15-18:00 (HG E 33.5)
2 h weekly

Offered In