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.)19SoSe 2021SoSe 2023
Computational Engineering Science (Informationstechnik im Maschinenwesen) (M. Sc.)17SoSe 2020SoSe 2023
Computer Engineering (M. Sc.)142SoSe 2020SoSe 2023
Computer Science (Informatik) (M. Sc.)135SoSe 2020SoSe 2023
Elektrotechnik (M. Sc.)121SoSe 2020SoSe 2023
ICT Innovation (M. Sc.)214SoSe 2020SoSe 2023
Information Systems Management (Wirtschaftsinformatik) (M. Sc.)213SoSe 2020SoSe 2023
Medieninformatik (M. Sc.)114SoSe 2020SoSe 2023
Physikalische Ingenieurwissenschaft (M. Sc.)215SoSe 2020SoSe 2023
Wirtschaftsingenieurwesen (B. Sc.)18SoSe 2020SoSe 2023
Wirtschaftsingenieurwesen (M. Sc.)124SoSe 2020SoSe 2023

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


No information