Afonso Sousa Bandeira: Katalogdaten im Herbstsemester 2023 |
Name | Herr Prof. Dr. Afonso Sousa 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 |
bandeira@math.ethz.ch | |
Departement | Mathematik |
Beziehung | Ordentlicher Professor |
Nummer | Titel | ECTS | Umfang | Dozierende | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
401-0131-00L | Lineare Algebra ![]() | 7 KP | 4V + 2U | B. Gärtner, A. Sousa Bandeira | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Kurzbeschreibung | Einführung in die lineare Algebra: Vektoren und Matrizen, Lösen linearer Gleichungssysteme, Vektorräume und Unterräume, Orthogonalität und kleinste Quadrate, Determinanten, Eigenwerte und Eigenvektoren, Singulärwert-Zerlegung, lineare Abbildungen. Anwendungen in und Bezüge zur Informatik werden begleitend präsentiert. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lernziel | - Fundamentale Konzepte der linearen Algebra verstehen und anwenden - Anwendungen der linearen Algebra in der Informatik kennenlernen | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Inhalt | Vektoren und Matrizen, Lösen linearer Gleichungssysteme, Vektorräume und Unterräume, Orthogonalität und kleinste Quadrate, Determinanten, Eigenwerte und Eigenvektoren, Singulärwert-Zerlegung, lineare Abbildungen. Anwendungen in und Bezüge zur Informatik. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literatur | Gilbert Strang, Introduction to Linear Algebra, 6th Edition, Wellesley - Cambridge Press. Weitere Literatur und Links finden sich auf der Vorlesungs-Webseite. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Kompetenzen![]() |
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
401-3940-73L | Student Seminar in Mathematics and Data ![]() Findet dieses Semester nicht statt. | 4 KP | 2S | A. Sousa Bandeira, Noch nicht bekannt | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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. Sousa Bandeira, A. Maillard | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 + 1A | A. Sousa Bandeira, A. Maillard | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 | M. Iacobelli, S. Mishra, R. Pandharipande, T. Rivière, A. Sousa Bandeira, 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, N. Meinshausen, J. Peters, R. Furrer, L. Held, T. Hothorn, D. Kozbur, A. Sousa Bandeira, M. Wolf | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Kurzbeschreibung | Research colloquium | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lernziel | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
401-5640-00L | ZüKoSt: Seminar on Applied Statistics ![]() | 0 KP | 1K | M. Kalisch, F. Balabdaoui, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. Mächler, L. Meier, N. Meinshausen, J. Peters, M. Robinson, A. Sousa Bandeira, C. Strobl | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Kurzbeschreibung | Etwa 3 Vorträge zur angewandten Statistik. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lernziel | Kennenlernen von statistischen Methoden in ihrer Anwendung in verschiedenen Anwendungsgebieten. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Inhalt | In etwa 3 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Kompetenzen![]() |
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
401-5660-00L | DACO Seminar ![]() | 0 KP | 1K | A. Sousa Bandeira, R. Zenklusen | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Kurzbeschreibung | Research colloquium | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lernziel | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
401-5680-00L | Foundations of Data Science Seminar ![]() | 0 KP | P. L. Bühlmann, H. Bölcskei, J. Peters, A. Sousa Bandeira, F. Yang | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Kurzbeschreibung | Research colloquium | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lernziel |