I030 – Applied artificial intelligence

Module
Applied artificial intelligence
Applied Artificial Intelligence
Module number
I030
Version: 1
Faculty
Informatics/Mathematics
Level
Master
Duration
1 Semester
Semester
Summer and Winter semester
Module supervisor

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

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

English
in "Applied Artificial Intelligence"
ECTS credits

5.00 credits

Workload

150 hours

Courses

4.00 SCH (2.00 SCH Lecture | 2.00 SCH Internship)
Self-study time

90.00 hours

Pre-examination(s)
None
Examination(s)

Alternative examination - Portfolio
Module examination | Weighting: 100% | tested in English language
in "Applied Artificial Intelligence"
Form of teaching
  • lecture
  • exercise
  • practical course
Media type
No information
Instruction content/structure
  • 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 
Qualification objectives
  • 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.
Social and personal skills
  • Working in teams
  • Self-awareness and evaluation of group processes
  • Problem solving across disciplines
  • Oral and written communication
Special admission requirements
Recommended prerequisites

Prior knowledge of programming, preferrably in Python.
Continuation options
Literature
  • 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. 
Current teaching resources

recent learning resources will be announced in the course and linked in the OPAL course
Notes
No information
Link to course/learning resources in OPAL
https://bildungsportal.sachsen.de/opal/auth/RepositoryEntry/41585803268
Close detail view
© Modulux (Version 4.3)