KI in der Produktionstechnik

MW2455Master's Modules5 ECTSGerman/Englishsummer semesterLehrstuhl für Werkzeugmaschinen und Fertigungstechnik (Prof. Zäh)

AI-edited module sheet. Based on the TUMonline module description, edited for readability.Original in TUMonline

What it is about

You learn fundamental methods of artificial intelligence and their application to production-technical questions. Based on the KDD process and industrial case studies, you practise data preparation, feature extraction, and the use of regression, classification, cluster, image processing, and reinforcement learning methods to derive knowledge from production data and derive actionable recommendations.

What you will be able to do

retrieve, compare and apply AI methods
identify central challenges of data-driven approaches
design solution concepts for industrial AI applications
design concepts for knowledge extraction from production and process data
apply the KDD process at all levels
evaluate AI application results for production-technical questions

What the module consists of

VorlesungConveying the theoretical foundations of the KDD process and methods; providing a slide deck
Übung (Gruppenarbeit)Application of the KDD process to industrially relevant datasets, discussion of methods/tools, project work

Teaching method

VorlesungExplain the theoretical foundations and present methods and tools
Übungen / Demonstrationen (Jupyter‑Notebooks)Instructions and practical examples for applying the tools and methods
GruppenprojektTransfer and independent application of the KDD process to industrial datasets; development of actionable recommendations

What you should already know

Höhere Mathematikrecommended
Statistikrecommended
Programmierkenntnisserecommended

Assessment

Written examProject workPresentationProject report

The module grade consists of a written exam (60 minutes, 60% of the module grade; permitted aids: writing utensils, ruler, non-programmable calculator) and a group project (40% of the module grade). In the group project you prepare a written project report (about 10 pages per group) and give an oral presentation (about 20 minutes per group). The report and presentation contribute equally to the project assessment. Individual contributions must be identified in the report and will be considered in the individual performance assessment. A voluntary grade bonus of 0.3 is possible through two accompanying learning portfolios (one individual and one group portfolio) if both are submitted in full and on time.

Reading list

C. M. Bishop - Pattern Recognition and Machine Learning
T. Hastie, R. Tibshirani, J. Friedman - The Elements of Statistical Learning

No dates in the current semester yet
There are no course dates for this module this semester yet. They usually get added during the semester.

Show past dates

LectureKI in der Produktionstechnik2 groups to choose from

ATue14:00–16:00
MW 0250, Hörsaal (5502.EG.250)
13× · 14.04.–14.07.
- 14.04.
- 21.04.
- 28.04.
- 05.05.
- 12.05.
- 19.05.
- 02.06.
- 09.06.
- 16.06.
- 23.06.
- 30.06.
- 07.07.
- 14.07.
BWed14:00–16:00
MW 1250, Hörsaal (5502.01.250)
13× · 15.04.–15.07.
- 15.04.
- 22.04.
- 06.05.
- 13.05.
- 20.05.
- 27.05.
- 03.06.
- 10.06.
- 17.06.
- 24.06.
- 01.07.
- 08.07.
- 15.07.

From an earlier semester, for reference only.

Show TUMonline data

Sprache: Deutsch/Englisch
Turnus: Sommersemester
Modulniveau: Master
Moduldauer: Einsemestrig
Gesamtstunden: 150
Präsenzstunden: 45
Eigenstudiumstunden: 105
Organisationsname: Lehrstuhl für Werkzeugmaschinen und Fertigungstechnik (Prof. Zäh)

Courses

KI in der Produktionstechnik
KI in der Produktionstechnik - Seminar
KI in der Produktionstechnik
KI in der Produktionstechnik - Seminar

Official page in TUMonline · Details are not binding.