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Core Concepts in Statistical Learning
Last Updated: 2026-06-03 00:14:13
Abstract
The course reviews the main concepts in statistical learning including statistical models, parametric and non-parametric inference, hypothesis testing and p-values, and classification. Additionally, it serves as a gentle introduction into some special topics such as the bootstrap, the EM-algorithm, causal inference and kernel estimation.
Objective
The main objects of the course are to first recall the foundations of sample spaces, random variables and their moments, joint distributions, concentration inequalities and modes of convergence. The second part of the course will dedicated to the foundations of statistical inference including the method of moments, maximum likelihood estimation, the EM-algorithm, hypothesis testing, p-values and corrections in multiple testing. In the third part, the focus will be put on estimation in regression models with the focus on linear and logistic links, testing independence of discrete outcomes, causal inference (introduction), non-parametric curve estimation (histograms and smooth kernel estimation for a probability density and a regression curve) and some chosen approaches in classification.
Resources
Literature
"All of Statistics" of L. Wassermann "Theory of Point Estimation" of E.L. Lehmann & G. Casella" "Statistical Inference" of G. Casella and R. Berger
General Information
- Language
- English
- Levels
- MSC , WBZ
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- written 120 minutes
- Aids
- None
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Core Concepts in Statistical Learning |
|
2 h weekly |
Offered In
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Electives (For the Master's degree in Applied Mathematics the following additional condition (not manifest in myStudies) must be obeyed: At least 14 of the required 26 credits from core courses and electives must be acquired in areas of applied mathematics and further application-oriented fields.)
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Statistics Master (The following courses belong to the curriculum of the Master's Programme in Statistics. The corresponding credits do not count as external credits even for course units where an enrolment at ETH Zurich is not possible.)
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