Afonso Bandeira: Katalogdaten im Frühjahrssemester 2021

NameHerr Prof. Dr. Afonso Bandeira
LehrgebietMathematik
Adresse
Professur für Mathematik
ETH Zürich, HG G 23.1
Rämistrasse 101
8092 Zürich
SWITZERLAND
Telefon+41 44 632 79 54
E-Mailbandeira@math.ethz.ch
DepartementMathematik
BeziehungOrdentlicher Professor

NummerTitelECTSUmfangDozierende
401-2684-00LMathematics of Machine Learning Information 5 KP2V + 1UA. Bandeira, N. Zhivotovskii
KurzbeschreibungIntroductory course to Mathematical aspects of Machine Learning, including Supervised Learning, Unsupervised Learning, Sparsity, and Online Learning.
LernzielIntroduction to Mathematical aspects of Machine Learning.
InhaltMathematical aspects of Supervised Learning, Unsupervised Learning, Sparsity, and Online Learning. This course is a Mathematical course, with Theorems and Proofs.
Voraussetzungen / BesonderesNote for non Mathematics students: this class requires a certain degree of mathematical maturity--including abstract thinking and the ability to understand and write proofs.
401-3940-21LStudent Seminar in Mathematics and Data: Optimal Transport Belegung eingeschränkt - Details anzeigen
Number of participants limited to 12.
4 KP2SA. Bandeira, G. Chinot
KurzbeschreibungThe 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
SkriptMore information, including list of papers, will be available at https://forum.math.ethz.ch/c/spring-2021/student-seminar-in-mathematics-and-data/51
LiteraturMore 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 / BesonderesThis 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.
401-4944-20LMathematics of Data Science
Findet dieses Semester nicht statt.
8 KP4GA. Bandeira
KurzbeschreibungMostly self-contained, but fast-paced, introductory masters level course on various theoretical aspects of algorithms that aim to extract information from data.
LernzielIntroduction to various mathematical aspects of Data Science.
InhaltThese 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.
Skripthttps://people.math.ethz.ch/~abandeira/BandeiraSingerStrohmer-MDS-draft.pdf
Voraussetzungen / BesonderesThe 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
401-5000-00LZurich Colloquium in Mathematics Information 0 KPR. Abgrall, A. Bandeira, M. Iacobelli, A. Iozzi, S. Mishra, R. Pandharipande, weitere Dozierende
KurzbeschreibungThe 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
401-5620-00LResearch Seminar on Statistics Information 0 KP1KP. L. Bühlmann, M. H. Maathuis, N. Meinshausen, S. van de Geer, A. Bandeira, R. Furrer, L. Held, T. Hothorn, D. Kozbur, M. Wolf
KurzbeschreibungForschungskolloquium
Lernziel
401-5640-00LZüKoSt: Seminar on Applied Statistics Information 0 KP1KM. 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
Kurzbeschreibung5 bis 6 Vorträge zur angewandten Statistik.
LernzielKennenlernen von statistischen Methoden in ihrer Anwendung in verschiedenen Gebieten, besonders in Naturwissenschaft, Technik und Medizin.
InhaltIn 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.
SkriptBei 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 / BesonderesDies 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.
401-5660-00LMath and Data (MAD+) Information 0 KPA. Bandeira, externe Veranstalter
KurzbeschreibungResearch colloquium
Lernziel
401-5680-00LFoundations of Data Science Seminar Information 0 KPP. 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
KurzbeschreibungResearch colloquium
Lernziel
401-5900-00LOptimization Seminar Information 0 KP1KA. Bandeira, R. Weismantel, R. Zenklusen
KurzbeschreibungLectures on current topics in optimization.
LernzielThis lecture series introduces graduate students to ongoing research activities (including applications) in the domain of optimization.
InhaltThis 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-AALComplex 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 KP13RA. Bandeira
KurzbeschreibungComplex 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
LiteraturL. 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 / BesonderesThe precise content changes with the examiner. Candidates must therefore contact the examiner in person before studying the material.