401-4944-20L  Mathematics of Data Science

SemesterSpring Semester 2021
LecturersA. Bandeira
Periodicityyearly recurring course
CourseDoes not take place this semester.
Language of instructionEnglish


401-4944-20 GMathematics of Data Science
Does not take place this semester.
Planned to take place again in the Autumn Semester 2021.
4 hrsA. Bandeira

Catalogue data

AbstractMostly self-contained, but fast-paced, introductory masters level course on various theoretical aspects of algorithms that aim to extract information from data.
ObjectiveIntroduction to various mathematical aspects of Data Science.
ContentThese 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.
Lecture noteshttps://people.math.ethz.ch/~abandeira/BandeiraSingerStrohmer-MDS-draft.pdf
Prerequisites / NoticeThe main mathematical tools used will be Probability, Linear Algebra (and real analysis), and a working knowledge of these subjects is required. In addition
to these prerequisites, this class requires a certain degree of mathematical maturity--including abstract thinking and the ability to understand and write proofs.

We encourage students who are interested in mathematical data science to take both this course and ``227-0434-10L Mathematics of Information'' taught by Prof. H. Bölcskei. The two courses are designed to be
A. Bandeira and H. Bölcskei

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits8 credits
ExaminersA. Bandeira
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 150 minutes
Additional information on mode of examinationThe examination of this course is only offered in the two examination sessions directly following the course.
Written aids10 A4 pages summary (or 5 A4 pages on both sides).
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

No public learning materials available.
Only public learning materials are listed.


No information on groups available.


There are no additional restrictions for the registration.

Offered in

Data Science MasterCore ElectivesWInformation
Electrical Engineering and Information Technology MasterCore SubjectsWInformation
Electrical Engineering and Information Technology MasterAdvanced Core CoursesWInformation
Mathematics BachelorSelection: Further RealmsWInformation
Mathematics MasterSelection: Further RealmsWInformation
Computational Science and Engineering MasterElectivesWInformation
Statistics MasterStatistical and Mathematical CoursesWInformation
Statistics MasterSubject Specific ElectivesWInformation