HTW Dresden - Apps: Module

I030 – Applied Artificial Intelligence

Modul

Applied Artificial Intelligence
Applied artificial intelligence

Modulnummer

I030
Version: 1

Fakultät

Informatik/Mathematik

Niveau

Master

Dauer

1 Semester

Turnus

Sommer- und Wintersemester

Modulverantwortliche/-r

Prof. Dr. rer. pol. Dirk Reichelt
dirk.reichelt(at)htw-dresden.de

Dozent/-in(nen)

Dr. Fouad Bahrpeyma
fouad.bahrpeyma(at)htw-dresden.de

Lehrsprache(n)

Englisch
in "Applied Artificial Intelligence"

ECTS-Credits

5.00 Credits

Workload

150 Stunden

Lehrveranstaltungen

4.00 SWS (2.00 SWS Vorlesung | 2.00 SWS Praktikum)

Selbststudienzeit

90.00 Stunden

Prüfungsvorleistung(en)

Keine

Prüfungsleistung(en)

Alternative Prüfungsleistung - Portfolio
Modulprüfung | Wichtung: 100% | wird in englischer Sprache abgenommen
in "Applied Artificial Intelligence"

Lehrform

lecture
exercise
practical course

Medienform

Keine Angabe

Lehrinhalte/Gliederung

Chapter 1 – A review of Python and data analysis
Chapter 2 – Machine learning techniques and applications
Chapter 3 – Regression and classification
Chapter 4 – Metaheuristics and optimization
Chapter 5 – Time series analysis and prediction
Chapter 6 – Computer vision
Chapter 7 – Control theory
Chapter 8 – Reinforcement learning
Chapter 9 – Predictive quality and maintenance
Chapter 10 – Robotics
Chapter 11 – Planning and scheduling
Chapter 12 – Advanced AI applications

Qualifikationsziele

Students know basic concepts of artificial intelligence theory.
Students know how to use artificial intelligence to accomplish tasks in a variety of fields.
Students will be able to use artificial intelligence methods to solve selected practical problems.
Students know concepts and methods for data preprocessing for machine learning. They can apply these practically.
Students will be able to analyze real world problems and select appropriate artificial intelligence methods to address them.

Sozial- und Selbstkompetenzen

Working in teams
Self-awareness and evaluation of group processes
Problem solving across disciplines
Oral and written communication

Besondere Zulassungsvoraussetzung

Empfohlene Voraussetzungen

Prior knowledge of programming, preferrably in Python.

Fortsetzungsmöglichkeiten

Literatur

Cranganu, Constantin, Henri Luchian, and Mihaela Elena Breaban, eds. Artificial intelligent approaches in petroleum geosciences. Switzerland: Springer International Publishing, 2015.
Bahrpeyma, Fouad, Hassan Haghighi, and Ali Zakerolhosseini. "An adaptive RL based approach for dynamic resource provisioning in Cloud virtualized data centers." Computing 97.12 (2015): 1209-1234.
Bahrpeyma, Fouad, Hassan Haghighi, and Ali Zakerolhosseini. "An adaptive RL based approach for dynamic resource provisioning in Cloud virtualized data centers." Computing 97.12 (2015): 1209-1234.
Bahrpeyma, Fouad, et al. "A methodology for validating diversity in synthetic time series generation." MethodsX 8 (2021): 101459.
Naylor, Thomas H., Terry G. Seaks, and Dean W. Wichern. "Box-Jenkins methods: An alternative to econometric models." International Statistical Review/Revue Internationale de Statistique (1972): 123-137.
Szeliski, Richard. Computer vision: algorithms and applications. Springer Nature, 2022.
Hamilton, James Douglas. Time series analysis. Princeton university press, 2020.
Allgöwer, Frank, and Alex Zheng, eds. Nonlinear model predictive control. Vol. 26. Birkhäuser, 2012.
Dong, Hao, et al. Deep Reinforcement Learning. Springer Singapore, 2020.
Halgamuge, Saman K., and Lipo Wang, eds. Classification and clustering for knowledge discovery. Vol. 4. Springer Science & Business Media, 2005.
Harrington, Peter. Machine learning in action. Simon and Schuster, 2012.

Aktuelle Lehrressourcen

recent learning resources will be announced in the course and linked in the OPAL course

Hinweise

Keine Angabe

Link zu Kurs/Lernressourcen im OPAL

https://bildungsportal.sachsen.de/opal/auth/RepositoryEntry/41585803268

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