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447-6255-00L 1 Credits WBZ D-MATH
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Analysis of High-Dimensional Data

High-Dimensional Statistics

Does not take place this semester. Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to . The Registrar's Office will then register you for the course.
VVZ CR n/a

Last Updated: 2026-02-05 16:14:51

Abstract

Block course on analysis of high-dimensional data with a focus on prediction and feature assessment.

Objective

The goal of this course is to gain a good understanding of the concepts discussed during the lecture and to apply the new methods on real data examples using the software "R". The topics covered in the lecture are: Part 1: Linear regression in the high-dimensional context; Overfitting, prediction and the bias-variance tradeoff; Model selection; Ridge and Lasso regularization Part 2: Logistic regression and regularization; Classification based on decision trees, Random Forest and AdaBoost; Multiple testing; P-value adjustment and variance shrinkage

Content

Course on Analysis of High-Dimensional Data with focus on Prediction and Feature Assessment. Part 1: Linear regression in the high-dimensional context; Overfitting, prediction and the bias-variance tradeoff; Model selection; Ridge and Lasso regularization Part 2: Logistic regression and regularization; Classification based on decision trees, Random Forest and AdaBoost; Multiple testing; P-value adjustment and variance shrinkage

Resources

Lecture Notes

The block course is based on lecture notes (https://bookdown.org/staedler_n/highdimstats/).

Literature

Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2001. The Elements of Statistical Learning. Springer Series in Statistics. New York, NY, USA: Springer New York Inc.

Learning Materials (Links)

General Information

Language
German
Levels
WBZ
Frequency
Every two years

Examination

Type
ungraded semester performance

Course Components

Type Title Time & Place Hours
lecture with exercise High-Dimensional Statistics
Does not take place this semester. Blockkurs. Weitere Informationen unter
No time listed 14 h semesterly

Offered In