Peter L. Bühlmann: Catalogue data in Autumn Semester 2017

Name Prof. Dr. Peter L. Bühlmann
Seminar für Statistik (SfS)
ETH Zürich, HG G 17
Rämistrasse 101
8092 Zürich
Telephone+41 44 632 73 38
Fax+41 44 632 12 28
RelationshipFull Professor

401-3620-67LStudent Seminar in Statistics: Computer Age Statistical Inference Restricted registration - show details
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.
4 credits2SM. H. Maathuis, P. L. Bühlmann, N. Meinshausen, S. van de Geer
AbstractWe study selected chapters from the book "Computer Age Statistical Inference: Algorithms, Evidence and Data Science" by Bradley Efron and Trevor Hastie.
ObjectiveDuring this seminar, we will study roughly one chapter per week from the book "Computer Age Statistical Inference: Algorithms, Evidence and Data Science" by Bradley Efron and Trevor Hastie. You will obtain a good overview of the field of modern statistics. Moreover, you will practice your self-studying and presentation skills.
ContentIn the words of Efron and Hastie: "The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. “Big data,” “data science,” and “machine learning” have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on a journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories – Bayesian, frequentist, Fisherian – individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The book integrates methodology and algorithms with statistical inference, and ends with speculation on the future direction of statistics and data science."
LiteratureBradley Efron and Trevor Hastie (2016). Computer Age Statistical Inference: Algorithms, Evidence and Data Science. Cambridge University Press, New York. ISBN: 9781107149892.
Prerequisites / NoticeWe 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.
401-3627-00LHigh-Dimensional Statistics
Does not take place this semester.
4 credits2VP. L. Bühlmann
Abstract"High-Dimensional Statistics" deals with modern methods and theory for statistical inference when the number of unknown parameters is of much larger order than sample size. Statistical estimation and algorithms for complex models and aspects of multiple testing will be discussed.
ObjectiveKnowledge of methods and basic theory for high-dimensional statistical inference
ContentLasso and Group Lasso for high-dimensional linear and generalized linear models; Additive models and many smooth univariate functions; Non-convex loss functions and l1-regularization; Stability selection, multiple testing and construction of p-values; Undirected graphical modeling
LiteraturePeter Bühlmann and Sara van de Geer (2011). Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer Verlag.
ISBN 978-3-642-20191-2.
Prerequisites / NoticeKnowledge of basic concepts in probability theory, and intermediate knowledge of statistics (e.g. a course in linear models or computational statistics).