Afonso Bandeira: Catalogue data in Spring Semester 2021

Name	Prof. Dr. Afonso Bandeira
Field	Mathematics
Address	Professur für Mathematik ETH Zürich, HG G 23.1 Rämistrasse 101 8092 Zürich SWITZERLAND
Telephone	+41 44 632 79 54
E-mail	bandeira@math.ethz.ch
Department	Mathematics
Relationship	Full Professor

Number	Title	ECTS	Hours	Lecturers
401-2684-00L	Mathematics of Machine Learning	5 credits	2V + 1U	A. Bandeira, N. Zhivotovskii
Abstract	Introductory course to Mathematical aspects of Machine Learning, including Supervised Learning, Unsupervised Learning, Sparsity, and Online Learning.
Objective	Introduction to Mathematical aspects of Machine Learning.
Content	Mathematical aspects of Supervised Learning, Unsupervised Learning, Sparsity, and Online Learning. This course is a Mathematical course, with Theorems and Proofs.
Prerequisites / Notice	Note for non Mathematics students: this class requires a certain degree of mathematical maturity--including abstract thinking and the ability to understand and write proofs.
401-3940-21L	Student Seminar in Mathematics and Data: Optimal Transport Number of participants limited to 12.	4 credits	2S	A. Bandeira, G. Chinot
Abstract	The Seminar starts with a basic introduction to Optimal Transport (including but not limited to: Monge and Kantorovich formulations, duality, Wassertstein distance). After the introductory material, each week will be devoted to either a research article in the topic or a more advanced concept. Particular emphasis will be given to applications to statistics and data science.
Objective
Lecture notes	More information, including list of papers, will be available at https://forum.math.ethz.ch/c/spring-2021/student-seminar-in-mathematics-and-data/51
Literature	More information, including list of papers, will be available at https://forum.math.ethz.ch/c/spring-2021/student-seminar-in-mathematics-and-data/51
Prerequisites / Notice	This seminar requires a certain degree of mathematical maturity--including abstract thinking and the ability to understand and write proofs. Probability theory and Linear Algebra is a required pre-requisite. Some basic familiarity with Optimization and Functional Analysis is beneficial.
401-4944-20L	Mathematics of Data Science Does not take place this semester.	8 credits	4G	A. Bandeira
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.
Lecture notes	https://people.math.ethz.ch/~abandeira/BandeiraSingerStrohmer-MDS-draft.pdf
Prerequisites / Notice	The 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 complementary. A. Bandeira and H. Bölcskei
401-5000-00L	Zurich Colloquium in Mathematics	0 credits		R. Abgrall, A. Bandeira, M. Iacobelli, A. Iozzi, S. Mishra, R. Pandharipande, further lecturers
Abstract	The 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.
Objective
401-5620-00L	Research Seminar on Statistics	0 credits	1K	P. L. Bühlmann, M. H. Maathuis, N. Meinshausen, S. van de Geer, A. Bandeira, R. Furrer, L. Held, T. Hothorn, D. Kozbur, M. Wolf
Abstract	Research colloquium
Objective
401-5640-00L	ZüKoSt: Seminar on Applied Statistics	0 credits	1K	M. Kalisch, F. Balabdaoui, A. Bandeira, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen, M. Robinson, C. Strobl, S. van de Geer
Abstract	5 to 6 talks on applied statistics.
Objective	Kennenlernen von statistischen Methoden in ihrer Anwendung in verschiedenen Gebieten, besonders in Naturwissenschaft, Technik und Medizin.
Content	In 5-6 Einzelvorträgen pro Semester werden Methoden der Statistik einzeln oder überblicksartig vorgestellt, oder es werden Probleme und Problemtypen aus einzelnen Anwendungsgebieten besprochen. 3 bis 4 der Vorträge stehen in der Regel unter einem Semesterthema.
Lecture notes	Bei manchen Vorträgen werden Unterlagen verteilt. Eine Zusammenfassung ist kurz vor den Vorträgen im Internet unter http://stat.ethz.ch/talks/zukost abrufbar. Ankündigunen der Vorträge werden auf Wunsch zugesandt.
Prerequisites / Notice	Dies ist keine Vorlesung. Es wird keine Prüfung durchgeführt, und es werden keine Kreditpunkte vergeben. Nach besonderem Programm. Koordinator M. Kalisch, Tel. 044 632 3435 Lehrsprache ist Englisch oder Deutsch je nach ReferentIn. Course language is English or German and may depend on the speaker.
401-5660-00L	Math and Data (MAD+)	0 credits		A. Bandeira, external organisers
Abstract	Research colloquium
Objective
401-5680-00L	Foundations of Data Science Seminar	0 credits		P. L. Bühlmann, A. Bandeira, H. Bölcskei, J. M. Buhmann, T. Hofmann, A. Krause, A. Lapidoth, H.‑A. Loeliger, M. H. Maathuis, N. Meinshausen, G. Rätsch, S. van de Geer, F. Yang
Abstract	Research colloquium
Objective
401-5900-00L	Optimization Seminar	0 credits	1K	A. Bandeira, R. Weismantel, R. Zenklusen
Abstract	Lectures on current topics in optimization.
Objective	This lecture series introduces graduate students to ongoing research activities (including applications) in the domain of optimization.
Content	This seminar is a forum for researchers interested in optimization theory and its applications. Speakers, invited from both academic and non-academic institutions, are expected to stimulate discussions on theoretical and applied aspects of optimization and related subjects. The focus is on efficient (or practical) 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.
406-2303-AAL	Complex Analysis Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit.	6 credits	13R	A. Bandeira
Abstract	Complex functions of one variable, Cauchy-Riemann equations, Cauchy theorem and integral formula, singularities, residue theorem, index of closed curves, analytic continuation, conformal mappings, Riemann mapping theorem.
Objective
Literature	L. Ahlfors: "Complex analysis. An introduction to the theory of analytic functions of one complex variable." International Series in Pure and Applied Mathematics. McGraw-Hill Book Co. B. Palka: "An introduction to complex function theory." Undergraduate Texts in Mathematics. Springer-Verlag, 1991. R.Remmert: Theory of Complex Functions.. Springer Verlag E.Hille: Analytic Function Theory. AMS Chelsea Publication
Prerequisites / Notice	The precise content changes with the examiner. Candidates must therefore contact the examiner in person before studying the material.