Afonso Bandeira: Catalogue data in Autumn Semester 2023

Name Prof. Dr. Afonso Bandeira
FieldMathematics
Address
Professur für Mathematik
ETH Zürich, HG G 23.1
Rämistrasse 101
8092 Zürich
SWITZERLAND
Telephone+41 44 632 79 54
E-mailbandeira@math.ethz.ch
DepartmentMathematics
RelationshipFull Professor

NumberTitleECTSHoursLecturers
401-0131-00LLinear Algebra Information 7 credits4V + 2UA. Bandeira, B. Gärtner
AbstractIntroduction to linear algebra: vectors and matrices, solving systems of linear equations, vector spaces and subspaces, orthogonality and least squares, determinants, eigenvalues and eigenvectors, singular value decomposition and linear transformations. Applications in and links to computer science will be presented in parallel.
Objective- Understand and apply fundamental concepts of linear algebra
- Learn about applications of linear algebra in computer science
ContentVectors and matrices, solving systems of linear equations, vector spaces and subspaces, orthogonality and least squares, determinants, eigenvalues and eigenvectors, singular value decomposition and linear transformations. Applications in and links to computer science.
LiteratureGilbert Strang, Introduction to Linear Algebra, 6th Edition, Wellesley - Cambridge Press. Further literature and links can be found on the course webpage.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingfostered
Media and Digital Technologiesfostered
Problem-solvingassessed
Project Managementfostered
Social CompetenciesCommunicationfostered
Cooperation and Teamworkfostered
Customer Orientationfostered
Leadership and Responsibilityfostered
Self-presentation and Social Influence fostered
Sensitivity to Diversityfostered
Negotiationfostered
Personal CompetenciesAdaptability and Flexibilityfostered
Creative Thinkingassessed
Critical Thinkingfostered
Integrity and Work Ethicsfostered
Self-awareness and Self-reflection fostered
Self-direction and Self-management fostered
401-3940-73LStudent Seminar in Mathematics and Data Restricted registration - show details
Does not take place this semester.
4 credits2SA. Bandeira, to be announced
Abstract
Objective
401-4944-DRLMathematics of Data Science Restricted registration - show details
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 Link with their name, course number and student ID. Please see Link
2 credits4GA. Bandeira, A. Maillard
AbstractMostly self-contained, but fast-paced, introductory masters level course on various theoretical aspects of algorithms that aim to extract information from data.
ObjectiveIntroduction to various mathematical aspects of Data Science.
ContentThese 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 noteshttps://people.math.ethz.ch/~abandeira/BandeiraSingerStrohmer-MDS-draft.pdf
Prerequisites / NoticeThe 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
401-4944-20LMathematics of Data Science8 credits4G + 1AA. Bandeira, A. Maillard
AbstractMostly self-contained, but fast-paced, introductory masters level course on various theoretical aspects of algorithms that aim to extract information from data.
ObjectiveIntroduction to various mathematical aspects of Data Science.
ContentThese 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 noteshttps://people.math.ethz.ch/~abandeira/BandeiraSingerStrohmer-MDS-draft.pdf
Prerequisites / NoticeThe 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 creditsM. Iacobelli, A. Bandeira, S. Mishra, R. Pandharipande, T. Rivière, University lecturers
AbstractThe 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
401-5620-00LResearch Seminar on Statistics Information 0 credits1KP. L. Bühlmann, N. Meinshausen, J. Peters, A. Bandeira, R. Furrer, L. Held, T. Hothorn, D. Kozbur
AbstractResearch colloquium
Objective
401-5640-00LZüKoSt: Seminar on Applied Statistics Information 0 credits1KM. Kalisch, F. Balabdaoui, A. Bandeira, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. Mächler, L. Meier, N. Meinshausen, J. Peters, M. Robinson, C. Strobl
AbstractAbout 3 talks on applied statistics.
ObjectiveSee how statistical methods are applied in practice.
ContentThere will be about 3 talks on how statistical methods are applied in practice.
Prerequisites / NoticeThis is no lecture. There is no exam and no credit points will be awarded. The current program can be found on the web:
http://stat.ethz.ch/events/zukost
Course language is English or German and may depend on the speaker.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesfostered
Techniques and Technologiesfostered
Method-specific CompetenciesDecision-makingfostered
Problem-solvingfostered
Personal CompetenciesCreative Thinkingfostered
Critical Thinkingfostered
401-5660-00LDACO Seminar Information 0 credits1KA. Bandeira, R. Zenklusen
AbstractResearch colloquium
Objective
401-5680-00LFoundations of Data Science Seminar Information 0 creditsP. L. Bühlmann, A. Bandeira, H. Bölcskei, J. Peters, F. Yang
AbstractResearch colloquium
Objective