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401-4944-DRL 2 Credits DR D-MATH

Mathematics of Data Science

Lecturers & Examiners: Prof. Dr. Afonso Sousa Bandeira, Dr. Antoine Maillard
Only for ZGSM (ETH D-MATH and UZH I-MATH) doctoral students. The latter need to register at myStudies and then send an email to with their name, course number and student ID. Please see
VVZ CR n/a

Last Updated: 2026-02-05 16:14:51

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 (Applied) Mathematics with: Computer Science, Electrical Engineering, Statistics, and/or Operations Research. Each lecture will feature a couple of Mathematical Open Problem(s) related to Data Science. 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

General Information

Language
English
Levels
DR
Frequency
Yearly recurring

Examination

Type
ungraded semester performance

Registration & Places

Priority: Registration for the course unit is only possible for the primary target group

Course Components

Type Title Time & Place Hours
lecture with exercise Mathematics of Data Science
  • Thu 12:15-14:00 (HG G 3)
  • Fri 10:15-12:00 (HG F 3)
4 h weekly

Offered In