- Introduction on different learning strategies
- Fundamentals and different types of regression networks
- Application on the simulation of physical phenomena (fluid dynamics)
- Statistics of different kinds of data
- Probabilistic and non-probabilistic methods
- Advanced applications on time-series analysis
- Fundamentals of reinforcement learning
- Different approaches of reinforcement learning
- Demonstration of reinforcement learning in games
Applications of Machine Learning and Data Science (M, 4.0 LP)
|Module Number||Module Name||CP (Effort)|
|INF-71-56-M-6||Applications of Machine Learning and Data Science||4.0 CP (120 h)|
|CP, Effort||4.0 CP = 120 h|
|Position of the semester||1 Sem. in WiSe|
|Level|| Master (General)|
|Area of study||[INF-KI] Intelligent Systems|
|Reference course of study||[INF-88.79-SG] M.Sc. Computer Science|
old title: Applications of Artificial Intelligence
|Type/SWS||Course Number||Title||Choice in |
|SL||SL is |
required for exa.
Applications of Machine Learning and Data Science
|P||42 h||78 h||
- About [INF-71-56-K-6]: Title: "Applications of Machine Learning and Data Science"; Presence-Time: 42 h; Self-Study: 78 h
- About [INF-71-56-K-6]:
The study achievement "[U-Schein] proof of successful participation in the exercise classes (ungraded)" must be obtained.
- It is a prerequisite for the examination for PL1.
Examination achievement PL1
- Form of examination: written exam (Klausur) (60-180 Min.)
- Examination Frequency: each semester
- Examination number: 67156 ("Applications of Machine Learning and Data Science")
Evaluation of grades
The grade of the module examination is also the module grade.
Competencies / intended learning achievements
Upon successful completion of the module, students will be able to
- explain the special features of industrially and economically relevant fields of application of the AI
- identify suitable applications of Artificial Intelligence in real contexts,
- identify concrete project requirements as problems of Artificial Intelligence,
- plan the application of advanced AI procedures in practice-oriented real-world environments
- analyze the performance of certain AI procedures
- reflect applications of Artificial Intelligence in real contexts.
- Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning . Vol. 1. No. 10. New York: Springer series in statistics, 2001.
- Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning . MIT press, 2016.
- Brockman, Greg, et al. "Openai gym." arXiv preprint arXiv:1606.01540 (2016).
- Cherkassky, Vladimir, and Filip M. Mulier. Learning from data: concepts, theory, and methods . John Wiley & Sons, 2007.
Requirements for attendance of the module (informal)
Requirements for attendance of the module (formal)None
References to Module / Module Number [INF-71-56-M-6]
|Course of Study||Section||Choice/Obligation|
|[EIT-88.A20-SG#2021] M.Sc. European Master in Embedded Computing Systems (EMECS) ||[Free Elective Area] Elective Subjects||[W] Elective Module|
|[EIT-88.?-SG#2021] M.Sc. Embedded Computing Systems (ESY) ||[Free Elective Area] Elective Subjects||[W] Elective Module|
|[INF-88.79-SG] M.Sc. Computer Science||[Specialisation] Specialization 1||[WP] Compulsory Elective|
|[EIT-AC-MSC-TW-MPOOL-7]||General Elective Modules Master A&C|
|[GS-CVT-CS-2022-E-MPOOL-6]||Catalog Electives Computer Science 2022|
|[GS-CVT-CS-E-MPOOL-6]||Catalog Electives Computer Science|
|[INF-SIAK-DT-AI-MPOOL-6]||SIAK Certificate "Digital Transformation" - Modules INF "Artificial Intelligence"|