Afonso Bandeira: Catalogue data in Autumn Semester 2021

Name Prof. Dr. Afonso Bandeira
Professur für Mathematik
ETH Zürich, HG G 23.1
Rämistrasse 101
8092 Zürich
Telephone+41 44 632 79 54
RelationshipFull Professor

401-3940-71LStudent Seminar in Mathematics and Data: Stochastic Optimization Restricted registration - show details
Number of participants limited to 12.
4 credits2SA. Bandeira, G. Chinot, N. Zhivotovskii
401-4944-20LMathematics of Data Science8 credits4GA. Bandeira
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 notes
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
401-5000-00LZurich Colloquium in Mathematics Information 0 creditsR. Abgrall, M. Iacobelli, A. Bandeira, A. Iozzi, S. Mishra, R. Pandharipande, University lecturers
AbstractThe lectures try to give an overview of "what is going on" in important areas of contemporary mathematics, to a wider non-specialised audience of mathematicians.
401-5620-00LResearch Seminar on Statistics Information 0 credits1KP. L. Bühlmann, M. H. Maathuis, N. Meinshausen, S. van de Geer, A. Bandeira, R. Furrer, L. Held, T. Hothorn, D. Kozbur, M. Wolf
AbstractResearch colloquium
401-5640-00LZüKoSt: Seminar on Applied Statistics Information 0 credits1KM. Kalisch, F. Balabdaoui, A. Bandeira, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, M. Robinson, C. Strobl, S. van de Geer
AbstractAbout 5 talks on applied statistics.
ObjectiveSee how statistical methods are applied in practice.
ContentThere will be about 5 talks on how statistical methods are applied in practice.
Prerequisites / NoticeThis is no lecture. There is no exam and no credit points will be awarded. The current program can be found on the web:
Course language is English or German and may depend on the speaker.
401-5660-00LDACO Seminar Information 0 credits1KA. Bandeira
AbstractResearch colloquium
401-5680-00LFoundations of Data Science Seminar Information 0 creditsP. L. Bühlmann, A. Bandeira, H. Bölcskei, F. Yang
AbstractResearch colloquium
401-5900-00LOptimization Seminar Information 0 creditsA. Bandeira, R. Weismantel, R. Zenklusen
AbstractLectures on current topics in optimization
ObjectiveExpose graduate students to ongoing research acitivites (including applications) in the domain of otimization.
ContentThis seminar is a forum for researchers interested in optimization theory and its applications. Speakers are expected to stimulate discussions on theoretical and applied aspects of optimization and related subjects. The focus is on efficient algorithms for continuous and discrete optimization problems, complexity analysis of algorithms and associated decision problems, approximation algorithms, mathematical modeling and solution procedures for real-world optimization problems in science, engineering, industries, public sectors etc.