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Statistics and Statistical Learning
(dt. Statistik und statistische Lernverfahren)

Level, degree of commitment Specialization module, compulsory elective module
Forms of teaching and learning,
workload		 Lecture (4 SWS), recitation class (2 SWS),
270 hours (90 h attendance, 180 h private study)
Credit points,
formal requirements		 9 CP
Course requirement(s): Successful completion of at least 50 percent of the points from the weekly exercises.
Examination type: Oral examination (individual examination) or written examination
Language,
Grading		 English,
The grading is done with 0 to 15 points according to the examination regulations for the degree program M.Sc. Business Mathematics.
Duration,
frequency		 One semester,
i.d.R. jedes Sommersemester
Person in charge of the module's outline Prof. Dr. Hajo Holzmann

Contents

  • Fundamentals of statistics: statistical models, estimation, testing, confidence intervals
  • Statistics in the linear model, the multivariate normal distribution
  • Exponential families and generalized linear models
  • Basics of supervised learning: classification and regression
  • Elements of unsupervised learning: cluster analysis and principal component analysis
  • Unbiased estimation and the Cramer - Rao bound

Qualification Goals

The students

  • can explain important methods of statistics and statistical learning and can analyze them mathematically,
  • can apply the methods to data sets using the statistics software R,
  • understand data analysis and statistics.

Prerequisites

None. The competences taught in the following modules are recommended: Elementary Probability and Statistics, Internship Stochastics.


Applicability

The module can be attended at FB12 in study program(s)

  • B.Sc. Mathematics
  • B.Sc. Business Mathematics
  • M.Sc. Data Science
  • M.Sc. Computer Science
  • M.Sc. Mathematics
  • M.Sc. Business Mathematics

When studying M.Sc. Business Mathematics, this module can be attended in the study area Free Compulsory Elective Modules.

The module can also be used in other study programs (export module).


Recommended Reading

  • Hastie, T., Tibshirani, R., Friedman, J. (2011). The Elements of Statistical Learning. Springer.



Please note:

This page describes a module according to the latest valid module guide in Winter semester 2025/26. Most rules valid for a module are not covered by the examination regulations and can therefore be updated on a semesterly basis. The following versions are available in the online module guide:

  • Winter 2016/17 (no corresponding element)
  • Summer 2018 (no corresponding element)
  • Winter 2018/19 (no corresponding element)
  • Winter 2019/20 (no corresponding element)
  • Winter 2020/21 (no corresponding element)
  • Summer 2021 (no corresponding element)
  • Winter 2021/22 (no corresponding element)
  • Winter 2022/23 (no corresponding element)
  • Winter 2023/24 (no corresponding element)
  • Winter 2025/26

The module guide contains all modules, independent of the current event offer. Please compare the current course catalogue in Marvin.

The information in this online module guide was created automatically. Legally binding is only the information in the examination regulations (Prüfungsordnung). If you notice any discrepancies or errors, we would be grateful for any advice.