Machine Intelligence: Concepts and Applications- M.Sc/ P.hD
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- Module: Machine Intelligence: Concepts and Applications
- Module number lecture: M.WIWI-WIN.0026
- UniVZ: 801478
- Cycle: Summer Semester
- Credits: 6 ECTS
- Courses of study: Master WI, Master Ufü, Master Economics, Master Applied Computer Science, Offer to all M.Sc/ P.hD
- Type of teaching and learning: Lecture (7 Block Courses)
- Examination: Project report (group work)
- Language: English
- Lecturers: Prof. Mohit Kumar (University of Rostock)
- Contact: Louisa Peters
Please check this website regularly for updates/changes.
| 24.03.2025, 8:00 - 13.04.2025 23:59 | Pre-registration via Stud.IP | First-come-first-served |
| 25.04.2025, 9:00 - 13:00 | Lecture 1 | online |
| 09.05.2025, 9:00 - 13:00 | Lecture 2 | online |
| 23.05.2025, 9:00 - 13:00 | Lecture 3 | online |
| 06.06.2025, 9:00 - 13:00 | Lecture 4 | online |
| 20.06.2025, 9:00 - 13:00 | Lecture 5 | online |
| 04.07.2025, 9:00 - 13:00 | Lecture 6 | online |
| 18.07.2025, 9:00 - 13:00 | Lecture 7 | online |
| 31.08.2025 | Project report (group work) | Online submission |
In summer term 2025, the course will be taught online via BigBlueButton. The BigBlueButton links can be found in the "Meetings"-section of the Stud.IP course. The lectures will not be recorded.
The number of participants is limited to 30 and prior pre-registration via Stud.IP is required. The pre-registration process is first-come-first-served. Please only pre-register if you intend to participate in the course.
Exam
The examination format is a project report (group work).
Learning objectives
The course will introduce the context of computational algorithms in broader areas of Machine Learning, Data Mining, Signal Processing, and Image Processing. The course would remain focused on the study of machine learning and fuzzy computing algorithms with practical applications to Computer Vision, eHealth & mHealth, and Water Distribution System. At the end of the course, the participants should be capable of applying intelligent computing algorithms to address the challenging issue of “uncertainties” in the real-world problems related to data modeling and analysis.
Contents:
• Artificial Intelligence and Machine Learning
• Stochastic Approach to Modeling
• Fuzzy Approach to Modeling
• Image Matching Applications
• Biomedical Signal Processing Applications in eHealth and mHealth
• Big Data Analysis Applications in Water Distribution System Modeling
• Privacy-Preserving Machine Learning