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401-4944-20L 8 Credits MSC D-ITET , D-MATH , D-INFK

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Mathematics of Data Science

Lecturers & Examiners: Prof. Dr. Afonso Sousa Bandeira

VVZ CR 4.33

Last Updated: 2026-06-01 11:30:51

Abstract

Mostly self-contained, but fast-paced, introductory masters level course on various theoretical aspects of algorithms that aim to extract information from data.

Objective

Introduction to various mathematical aspects of Data Science.

Content

These topics lie in overlaps of Mathematics with: Computer Science, Electrical Engineering, Statistics, and/or Operations Research. Most lectures will feature Mathematical Open Problems. The main mathematical tools used will be Probability and Linear Algebra, and a basic familiarity with these subjects is required. There will also be some (although knowledge of these tools is not assumed) Graph Theory, Representation Theory, Applied Harmonic Analysis, among others. The topics treated will include Dimension reduction, Manifold learning, Sparse recovery, Random Matrices, Approximation Algorithms, Community detection in graphs, and several others.

Resources

Lecture Notes

https://people.math.ethz.ch/~abandeira/BandeiraSingerStrohmer-MDS-draft.pdf(seehttps://people.math.ethz.ch/~abandeira//TenLecturesFortyTwoProblems.pdffor some Open Problems)

General Information

Language: English
Levels: MSC
Frequency: Yearly recurring

Examination

Type: session examination
Mode: written 180 minutes
Aids: None

The examination of this course is only offered in the two examination sessions directly following the course.A bonus of up to 0.25 grade points can be achieved by a combination of participation (such as a short presentation at the last lecture, a short report on a paper or open problem) and 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.)

Course Components

Type	Title	Time & Place	Hours
lecture	Mathematics of Data Science	Fri 10:15-13:00 (HG D 7.1)	3 h weekly
exercise	Mathematics of Data Science Groups are selected in myStudies. Mon 14-16 or Thu 12-14	Mon 14:15-16:00 (RZ F 21) Thu 12:15-14:00 (HG D 1.2)	2 h weekly

Offered In

Rechnergestützte Wissenschaften Master
- Wahlfächer (Von den angebotenen Wahlfächern müssen mindestens zwei Lerneinheiten erfolgreich abgeschlossen werden. Als Wahlfächer für Rechnergestützte Wissenschaften Master gelten automatisch (ohne Anrechnungsgesuch) auch alle Kernfächer/Vertiefungsfächer (aber nicht Wahlfächer!) aus folgenden Studiengängen: Informatik Master Mathematik Master Physik Master Elektrotechnik und Informationstechnologie Master Data Science Master Robotics, Systems and Control Master Statistik Master Neural Systems and Computation Master gemäss den angegebenen Abschnittsreferenzen.)
Mathematik Master
- Kernfächer (Für das Master-Diplom in Angewandter Mathematik ist die folgende Zusatzbedingung (nicht in myStudies ersichtlich) zu beachten: Mindestens 14 KP der erforderlichen 26 KP aus Kern- und Wahlfächern müssen aus Bereichen der angewandten Mathematik und weiteren anwendungsorientierten Gebieten stammen.)
  - Kernfächer aus Bereichen der angewandten Mathematik (vollständiger Titel: Kernfächer aus Bereichen der angewandten Mathematik und weiteren anwendungsorientierten Gebieten)
Elektrotechnik und Informationstechnologie Master
- 01 - Fächer der Vertiefungen
  - Vertiefung: Signal Processing and Machine Learning (The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Signal Processing and Machine Learning ", see . The individual study plan is subject to the tutor's approval.)
    - Kernfächer (These core courses are particularly recommended for the field of "Signal Processing and Machine Learning". You may choose core courses form other fields in agreement with your tutor. A minimum of 24 credits must be obtained from core courses during the MSc EEIT.)
      - Advanced Core Courses
Statistik Master (Die hier aufgelisteten Lehrveranstaltungen gehören zum Curriculum des Master-Studiengangs Statistik. Die entsprechenden KP gelten nicht als Mobilitäts-KP, auch wenn gewisse Lerneinheiten nicht an der ETH Zürich belegt werden können.)
- Fachbezogene Wahlfächer
Data Science Master
- Master-Studium (Studienreglement 2023)
  - Kernfächer
    - Datenanalyse
- Master-Studium (Studienreglement 2017)
  - Kernfächer
    - Wählbare Kernfächer