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Machine Learning and Identification

Module name (EN):
Name of module in study programme. It should be precise and clear.
Machine Learning and Identification
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Industrial Engineering, Master, ASPO 01.10.2021
Module code: WiMb21NT108
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
1SU+3PA (4 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
6
Semester: 1
Mandatory course: no
Language of instruction:
German
Assessment:
Project work

[updated 21.06.2021]
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
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):
None.
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Frank Kneip
Lecturer: Prof. Dr. Frank Kneip

[updated 28.01.2020]
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]
 
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