Module Number

Module Title

Introduction to Statistical Machine Learning for Bioinformaticians and Medical Informaticians
Type of Module

Elective Compulsory
Work load
- Contact time
- Self study
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction English
Type of Exam

Oral or written exam depending on number of participants; 60% exercise points as pre-requisite; a limited amount of exercise points may count as bonus points in the exam.

Lecture type(s) Lecture, Tutorial

This lecture provides an introduction into statistical machine learning with a
focus on practical application in biomedical data analysis. It comprises basic
methods for supervised (classification, regression) and unsupervised learning.
Topics include but are not limited to:
• Linear models for regression and classification
• Model selection & regularization
• Resampling methods: Cross validation & Bootstrap
• Decision trees
• Ensemble methods: Random Forest & Boosting
• Support vector machines
• Dimensionality reduction
• Clustering algorithms


The students are capable of explaining the most important terms, problems,
and algorithms in statistical machine learning. They are able to decide which
type of methods is promising for analyzing a particular biomedical data set and
to justify that decision by theoretical arguments and empirical studies.

Allocation of credits / grading
Type of Class
Type of Exam
Exam duration
of Module (%)
Prerequisite for participation There are no specific prerequisites.
Lecturer / Other Eggeling, Pfeifer

Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani: An Introduction to Statistical Learning with Applications in R, Springer Texts in Statistics.

Further books will be announced in the first lecture.

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas BIOINFM2210, BIOINFM2510, INFM2510, MDZINFM2510, MDZINFM3110, MEINFM3210