Marloes H. Maathuis: Catalogue data in Autumn Semester 2018 |
| Name | Prof. Dr. Marloes H. Maathuis |
| Field | Statistics |
| URL | http://stat.ethz.ch/~maathuis |
| Department | Mathematics |
| Relationship | Full Professor |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 401-0603-00L | Stochastics (Probability and Statistics)  | 4 credits | 2V + 1U | M. H. Maathuis | |
| Abstract | This class covers the following concepts: random variables, probability, discrete and continuous distributions, joint and conditional probabilities and distributions, the law of large numbers, the central limit theorem, descriptive statistics, statistical inference, inference for normally distributed data, point estimation, and two-sample tests. | ||||
| Learning objective | Knowledge of the basic principles of probability and statistics. | ||||
| Content | Introduction to probability theory, some basic principles from mathematical statistics and basic methods for applied statistics. | ||||
| Lecture notes | Lecture notes | ||||
| Literature | Lecture notes | ||||
| 401-3620-68L | Student Seminar in Statistics: Statistical Learning with Sparsity  Number of participants limited to 24. Mainly for students from the Mathematics Bachelor and Master Programmes who, in addition to the introductory course unit 401-2604-00L Probability and Statistics, have heard at least one core or elective course in statistics. Also offered in the Master Programmes Statistics resp. Data Science. | 4 credits | 2S | M. Mächler, M. H. Maathuis, N. Meinshausen, S. van de Geer | |
| Abstract | We study selected chapters from the 2015 book "Statistical Learning with Sparsity" by Trevor Hastie, Rob Tibshirani and Martin Wainwright. (details see below) | ||||
| Learning objective | During this seminar, we will study roughly one chapter per week from the book. You will obtain a good overview of the field of sparse & high-dimensional modeling of modern statistics. Moreover, you will practice your self-studying and presentation skills. | ||||
| Content | (From the book's preface:) "... summarize the actively developing field of statistical learning with sparsity. A sparse statistical model is one having only a small number of nonzero parameters or weights. It represents a classic case of “less is more”: a sparse model can be much easier to estimate and interpret than a dense model. In this age of big data, the number of features measured on a person or object can be large, and might be larger than the number of observations. The sparsity assumption allows us to tackle such problems and extract useful and reproducible patterns from big datasets." For presentation of the material, occasionally you'd consider additional published research, possibly e.g., for "High-Dimensional Inference" | ||||
| Lecture notes | Website: with groups, FAQ, topics, slides, and Rscripts : https://stat.ethz.ch/lectures/as18/seminar.php#course_materials | ||||
| Literature | Trevor Hastie, Robert Tibshirani, Martin Wainwright (2015) Statistical Learning with Sparsity: The Lasso and Generalization Monographs on Statistics and Applied Probability 143 Chapman Hall/CRC ISBN 9781498712170 Access : - https://www.taylorfrancis.com/books/9781498712170 (full access via ETH (library) network, if inside ETH (VPN)) - Author's website (includes errata, updated pdf, data): https://web.stanford.edu/~hastie/StatLearnSparsity/ | ||||
| Prerequisites / Notice | We require at least one course in statistics in addition to the 4th semester course Introduction to Probability and Statistics, as well as some experience with the statistical software R. Topics will be assigned during the first meeting. | ||||
| 401-5620-00L | Research Seminar on Statistics | 0 credits | 2K | L. Held, T. Hothorn, D. Kozbur, M. H. Maathuis, N. Meinshausen, S. van de Geer, M. Wolf | |
| Abstract | Research colloquium | ||||
| Learning objective | |||||
| 401-5640-00L | ZüKoSt: Seminar on Applied Statistics | 0 credits | 1K | M. Kalisch, R. Furrer, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen, M. Robinson, C. Strobl, S. van de Geer | |
| Abstract | About 5 talks on applied statistics. | ||||
| Learning objective | See how statistical methods are applied in practice. | ||||
| Content | There will be about 5 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. | ||||