English
I030 – Applied Artificial Intelligence
Applied artificial intelligence
Version: 1
Prof. Dr. rer. pol. Dirk Reichelt
dirk.reichelt(at)htw-dresden.de
Dr. Fouad Bahrpeyma
fouad.bahrpeyma(at)htw-dresden.de
Englisch
5.00 Credits
150 Stunden
4.00 SWS (2.00 SWS Vorlesung | 2.00 SWS Praktikum)
90.00 Stunden
Alternative Prüfungsleistung - Portfolio
Wichtung: 100 % | Wird in englischer Sprache abgenommen
- lecture
- exercise
- practical course
- 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
- 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.
- Working in teams
- Self-awareness and evaluation of group processes
- Problem solving across disciplines
- Oral and written communication
Prior knowledge of programming, preferrably in Python.
- 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.
recent learning resources will be announced in the course and linked in the OPAL course