Afonso Bandeira: Katalogdaten im Frühjahrssemester 2021

Name	Herr Prof. Dr. Afonso Bandeira
Lehrgebiet	Mathematik
Adresse	Professur für Mathematik ETH Zürich, HG G 23.1 Rämistrasse 101 8092 Zürich SWITZERLAND
Telefon	+41 44 632 79 54
E-Mail	bandeira@math.ethz.ch
Departement	Mathematik
Beziehung	Ordentlicher Professor

Nummer	Titel	ECTS	Umfang	Dozierende
401-2684-00L	Mathematics of Machine Learning	5 KP	2V + 1U	A. Bandeira, N. Zhivotovskii
Kurzbeschreibung	Introductory course to Mathematical aspects of Machine Learning, including Supervised Learning, Unsupervised Learning, Sparsity, and Online Learning.
Lernziel	Introduction to Mathematical aspects of Machine Learning.
Inhalt	Mathematical aspects of Supervised Learning, Unsupervised Learning, Sparsity, and Online Learning. This course is a Mathematical course, with Theorems and Proofs.
Voraussetzungen / Besonderes	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 KP	2S	A. Bandeira, G. Chinot
Kurzbeschreibung	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.
Lernziel
Skript	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
Literatur	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
Voraussetzungen / Besonderes	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 Findet dieses Semester nicht statt.	8 KP	4G	A. Bandeira
Kurzbeschreibung	Mostly self-contained, but fast-paced, introductory masters level course on various theoretical aspects of algorithms that aim to extract information from data.
Lernziel	Introduction to various mathematical aspects of Data Science.
Inhalt	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.
Skript	https://people.math.ethz.ch/~abandeira/BandeiraSingerStrohmer-MDS-draft.pdf
Voraussetzungen / Besonderes	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 KP		R. Abgrall, A. Bandeira, M. Iacobelli, A. Iozzi, S. Mishra, R. Pandharipande, weitere Dozierende
Kurzbeschreibung	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.
Lernziel
401-5620-00L	Research Seminar on Statistics	0 KP	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
Kurzbeschreibung	Forschungskolloquium
Lernziel
401-5640-00L	ZüKoSt: Seminar on Applied Statistics	0 KP	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
Kurzbeschreibung	5 bis 6 Vorträge zur angewandten Statistik.
Lernziel	Kennenlernen von statistischen Methoden in ihrer Anwendung in verschiedenen Gebieten, besonders in Naturwissenschaft, Technik und Medizin.
Inhalt	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.
Skript	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.
Voraussetzungen / Besonderes	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 KP		A. Bandeira, externe Veranstalter
Kurzbeschreibung	Research colloquium
Lernziel
401-5680-00L	Foundations of Data Science Seminar	0 KP		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
Kurzbeschreibung	Research colloquium
Lernziel
401-5900-00L	Optimization Seminar	0 KP	1K	A. Bandeira, R. Weismantel, R. Zenklusen
Kurzbeschreibung	Lectures on current topics in optimization.
Lernziel	This lecture series introduces graduate students to ongoing research activities (including applications) in the domain of optimization.
Inhalt	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 Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben. Alle anderen Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.	6 KP	13R	A. Bandeira
Kurzbeschreibung	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.
Lernziel
Literatur	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
Voraussetzungen / Besonderes	The precise content changes with the examiner. Candidates must therefore contact the examiner in person before studying the material.