Display language

#40550 / #6

Seit SoSe 2020


Machine Learning 1


Müller, Klaus-Robert


Schriftliche Prüfung


Fakultät IV

Institut für Softwaretechnik und Theoretische Informatik

34352000 FG Maschinelles Lernen

No information


MAR 4-1

Montavon, Gregoire


Learning Outcomes

The students are able to independently apply methods from machine learning on new data. This includes methods for classification, regression, dimensionality reduction and clustering. Moreover, the module teaches the mathematical skills (probability theory, optimization theory) needed to extend and theoretical analyze machine learning methods.


Probability theory, theory of estimation (e.g. Maximum likelihood, EM algorithm) Methods from Machine Learning: Dimensionality reduction (PCA, ICA), Clustering, Supervised learning (e.g. Regression, LDA, SVM, Gaussian processes)

Module Components


All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Maschinelles Lernen IIV0434 L 501WiSeEnglish6

Workload and Credit Points

Maschinelles Lernen I (IV):

Workload descriptionMultiplierHoursTotal
270.0h(~9 LP)
Concepts & Theory15.06.0h90.0h
The Workload of the module sums up to 270.0 Hours. Therefore the module contains 9 Credits.

Description of Teaching and Learning Methods

weekly lectures, exercise sessions, and homeworks

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Basic knowledge in linear algebra and calculus. Basic knowledge in probability theory. Basic programming knowledge, programming in Python.

Mandatory requirements for the module test application:

1. Requirement
Unbenoteter Übungsschein

Module completion



Type of exam

Written exam




120 min

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:

Maximum Number of Participants

This module is not limited to a number of students.

Registration Procedures

cf. course webpage

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable


Electronical lecture notes

Availability:  unavailable



Recommended literature
No recommended literature given

Assigned Degree Programs

This module is used in the following Degree Programs (new System):

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Biomedizinische Technik (M. Sc.)28SoSe 2021WiSe 2022/23
Computational Engineering Science (Informationstechnik im Maschinenwesen) (M. Sc.)16SoSe 2020WiSe 2022/23
Computer Engineering (M. Sc.)136SoSe 2020WiSe 2022/23
Computer Science (Informatik) (M. Sc.)130SoSe 2020WiSe 2022/23
Elektrotechnik (M. Sc.)118SoSe 2020WiSe 2022/23
ICT Innovation (M. Sc.)212SoSe 2020WiSe 2022/23
Information Systems Management (Wirtschaftsinformatik) (M. Sc.)212SoSe 2020WiSe 2022/23
Medieninformatik (M. Sc.)112SoSe 2020WiSe 2022/23
Physikalische Ingenieurwissenschaft (M. Sc.)211SoSe 2020WiSe 2022/23
Wirtschaftsingenieurwesen (B. Sc.)17SoSe 2020WiSe 2022/23
Wirtschaftsingenieurwesen (M. Sc.)121SoSe 2020WiSe 2022/23

Students of other degrees can participate in this module without capacity testing.


No information