## Afonso Bandeira: Catalogue data in Autumn Semester 2023 |

Name | Prof. Dr. Afonso Bandeira |

Field | Mathematics |

Address | Professur für Mathematik ETH Zürich, HG G 23.1 Rämistrasse 101 8092 Zürich SWITZERLAND |

Telephone | +41 44 632 79 54 |

bandeira@math.ethz.ch | |

Department | Mathematics |

Relationship | Full Professor |

Number | Title | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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401-0131-00L | Linear Algebra | 7 credits | 4V + 2U | A. Bandeira, B. Gärtner | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Abstract | Introduction 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Content | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Literature | Gilbert Strang, Introduction to Linear Algebra, 6th Edition, Wellesley - Cambridge Press. Further literature and links can be found on the course webpage. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Competencies |
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401-3940-73L | Student Seminar in Mathematics and Data Does not take place this semester. | 4 credits | 2S | A. Bandeira, to be announced | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Abstract | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Objective | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

401-4944-DRL | Mathematics of Data Science | 2 credits | 4G | A. Bandeira, A. Maillard | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Abstract | Mostly self-contained, but fast-paced, introductory masters level course on various theoretical aspects of algorithms that aim to extract information from data. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Objective | Introduction to various mathematical aspects of Data Science. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Content | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lecture notes | https://people.math.ethz.ch/~abandeira/BandeiraSingerStrohmer-MDS-draft.pdf | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Prerequisites / Notice | 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 credits | 4G + 1A | A. Bandeira, A. Maillard | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Abstract | Mostly self-contained, but fast-paced, introductory masters level course on various theoretical aspects of algorithms that aim to extract information from data. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Objective | Introduction to various mathematical aspects of Data Science. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Content | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lecture notes | https://people.math.ethz.ch/~abandeira/BandeiraSingerStrohmer-MDS-draft.pdf | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Prerequisites / Notice | 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 credits | M. Iacobelli, A. Bandeira, S. Mishra, R. Pandharipande, T. Rivière, University lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Abstract | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Objective | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

401-5620-00L | Research Seminar on Statistics | 0 credits | 1K | P. L. Bühlmann, N. Meinshausen, J. Peters, A. Bandeira, R. Furrer, L. Held, T. Hothorn, D. Kozbur | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Abstract | Research colloquium | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Objective | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

401-5640-00L | ZüKoSt: Seminar on Applied Statistics | 0 credits | 1K | M. 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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Abstract | About 3 talks on applied statistics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Objective | See how statistical methods are applied in practice. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Content | There will be about 3 talks on how statistical methods are applied in practice. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Prerequisites / Notice | This 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Competencies |
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401-5660-00L | DACO Seminar | 0 credits | 1K | A. Bandeira, R. Zenklusen | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Abstract | Research colloquium | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Objective | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

401-5680-00L | Foundations of Data Science Seminar | 0 credits | P. L. Bühlmann, A. Bandeira, H. Bölcskei, J. Peters, F. Yang | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Abstract | Research colloquium | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Objective |