VVZ API is not affiliated with ETH Zurich. Data might be outdated or incorrect. Please view the official ETHZ Vorlesungsverzeichnis for binding information.
Multivariate Statistics and Machine Learning
Multivariate Statistik und Machine Learning
Last Updated: 2026-02-05 16:22:39
Abstract
Introduction to statistical modelling and machine learning.
Objective
The goal is to familiarise students with the principles and tools of machine learning, and to enable them to apply them for practical data analysis.
Content
multivariate probability distributions; comparison of distributions; regression; classification; model selection and cross-validation; clustering and density estimation; mixture models; neural networks
Resources
Literature
C. Bishop: Pattern Recognition and Machine Learning, Springer 2006 T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Springer 2017 R. Duda, P. Hart, D. Stork: Pattern Classification, Wiley 2000
General Information
- Language
- German
- Levels
- BSC
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- written 90 minutes
- Aids
- None
- Digital
- The exam takes place on devices provided by ETH Zurich.
Registration & Places
- Max Places
- 50
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Multivariate Statistik und Machine Learning |
|
3 h weekly |
Offered In
-
Geospatial Engineering Bachelor (Registration via myStudies for a thesis during spring semester until 15 Januaryt at the latest, for a thesis during autumn semester until 15 August at the latest.)
-