Jonas Peters: Catalogue data in Autumn Semester 2023

Name Prof. Dr. Jonas Peters
FieldStatistics
Address
Professur für Statistik
ETH Zürich, HG G 12
Rämistrasse 101
8092 Zürich
SWITZERLAND
Telephone+41 44 632 75 84
E-mailjonas.peters@stat.math.ethz.ch
DepartmentMathematics
RelationshipFull Professor

NumberTitleECTSHoursLecturers
401-4632-DRLCausality Information 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 info@zgsm.ch with their name, course number and student ID. Please see https://zgsm.math.uzh.ch/index.php?id=forum0
2 credits2GJ. Peters
AbstractIn statistics, we are used to search for the best predictors of some random variable. In many situations, however, we are interested in predicting a system's behavior under manipulations. For such an analysis, we require knowledge about the underlying causal structure of the system. In this course, we study concepts and theory behind causal inference.
Learning objectiveAfter this course, you should be able to
- understand the language and concepts of causal inference
- know the assumptions under which one can infer causal relations from observational and/or interventional data
- describe and apply different methods for causal structure learning
- given data and a causal structure, derive causal effects and predictions of interventional experiments
ContentThe material covered in this course has a significant overlap with the
material that has been covered in 401-3620-22L Student Seminar in
Statistics: Causality FS2023.
LiteratureParts of this course will be based on the book "Elements of Causal Inference" (MIT Press, open access). More details will follow.
Prerequisites / NoticePrerequisites: basic knowledge of probability theory and regression
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
401-4632-15LCausality Information 4 credits2GJ. Peters
AbstractIn statistics, we are used to search for the best predictors of some random variable. In many situations, however, we are interested in predicting a system's behavior under manipulations. For such an analysis, we require knowledge about the underlying causal structure of the system. In this course, we study concepts and theory behind causal inference.
Learning objectiveAfter this course, you should be able to
- understand the language and concepts of causal inference
- know the assumptions under which one can infer causal relations from observational and/or interventional data
- describe and apply different methods for causal structure learning
- given data and a causal structure, derive causal effects and predictions of interventional experiments
ContentThe material covered in this course has a significant overlap with the
material that has been covered in 401-3620-22L Student Seminar in
Statistics: Causality FS2023.
LiteratureParts of this course will be based on the book "Elements of Causal Inference" (MIT Press, open access). More details will follow.
Prerequisites / NoticePrerequisites: basic knowledge of probability theory and regression
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
401-5620-00LResearch Seminar on Statistics Information 0 credits1KP. L. Bühlmann, N. Meinshausen, J. Peters, R. Furrer, L. Held, T. Hothorn, D. Kozbur, A. Sousa Bandeira, M. Wolf
AbstractResearch colloquium
Learning objective
401-5640-00LZüKoSt: Seminar on Applied Statistics Information 0 credits1KM. 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
AbstractAbout 3 talks on applied statistics.
Learning 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
401-5680-00LFoundations of Data Science Seminar Information 0 creditsP. L. Bühlmann, H. Bölcskei, J. Peters, A. Sousa Bandeira, F. Yang
AbstractResearch colloquium
Learning objective