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Machine Learning and Identification

Module name (EN): Machine Learning and Identification
Degree programme: Industrial Engineering, Master, ASPO 01.10.2019
Module code: WiMb19NT108
Hours per semester week / Teaching method: 1SU+3PA (4 hours per week)
ECTS credits: 6
Semester: 1
Mandatory course: no
Language of instruction:
German
Assessment:
Project work

[updated 21.06.2021]
Workload:
60 class hours (= 45 clock hours) over a 15-week period.
The total student study time is 150 hours (equivalent to 6 ECTS credits).
There are therefore 105 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
WiMb19NT106


[updated 10.02.2021]
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Frank Kneip
Lecturer:
Prof. Dr. Frank Kneip


[updated 10.02.2021]
Learning outcomes:
        After successfully completing this moduel, students will be familiar with machine learning and identification.
        They will have in-depth knowledge about parameter and state estimation procedures.
        They will be able to determine states of a system (e.g. a technical machine or an economic system) and/or its parameterization uisng the available data sets.

[updated 21.06.2021]
Module content:
        Linear regression
        Iterative methods
        Parameter identification method
        State estimates of a dynamic system

[updated 21.06.2021]
Teaching methods/Media:
        Lecture introduction to machine learning and identification (esp. state and parameter estimation)
        Independent project work/case studies under supervision
        Discussions between students and lecturers
        The results of the students’ project work must be documented in a suitable form (written paper and presentation).

[updated 21.06.2021]
Recommended or required reading:
        Will be announced at the beginning of the module.

[updated 21.06.2021]
[Mon Nov 29 00:10:19 CET 2021, CKEY=wmlui, BKEY=wtm, CID=WiMb19NT108, LANGUAGE=en, DATE=29.11.2021]