Afonso Bandeira: Katalogdaten im Herbstsemester 2022

Name	Herr Prof. Dr. Afonso Bandeira
Lehrgebiet	Mathematik
Adresse	Institut für Operations Research 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
401-3940-72L	Student Seminar in Mathematics and Data: Differential Privacy Number of participants limited to 12.	4 KP	2S	A. Bandeira, M. T. Boedihardjo
Kurzbeschreibung
Lernziel
401-4944-DRL	Mathematics of Data Science Only for ZGSM (ETH D-MATH and UZH I-MATH) doctoral students. The latter need to register at myStudies and then send an email to info@zgsm.ch with their name, course number and student ID. Please see https://zgsm.math.uzh.ch/index.php?id=forum0	2 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
401-4944-20L	Mathematics of Data Science	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, M. Iacobelli, A. Bandeira, A. Iozzi, S. Mishra, R. Pandharipande, Uni-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	Research colloquium
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	Etwa 5 Vorträge zur angewandten Statistik.
Lernziel	Kennenlernen von statistischen Methoden in ihrer Anwendung in verschiedenen Anwendungsgebieten.
Inhalt	In etwa 5 Einzelvorträgen pro Semester werden Methoden der Statistik einzeln oder überblicksartig vorgestellt, oder es werden Probleme und Problemtypen aus einzelnen Anwendungsgebieten besprochen.
Voraussetzungen / Besonderes	Dies ist keine Vorlesung. Es wird keine Prüfung durchgeführt, und es werden keine Kreditpunkte vergeben. Nach besonderem Programm: http://stat.ethz.ch/events/zukost Lehrsprache ist Englisch oder Deutsch je nach ReferentIn.
401-5660-00L	DACO Seminar	0 KP		A. Bandeira, R. Weismantel, R. Zenklusen
Kurzbeschreibung	Research colloquium
Lernziel
401-5680-00L	Foundations of Data Science Seminar	0 KP		P. L. Bühlmann, A. Bandeira, H. Bölcskei, S. van de Geer, F. Yang
Kurzbeschreibung	Research colloquium
Lernziel