Machine Learning (MLE)
6/7. Semester
5 ECTS | 4 SWS
Written Exam 60 Minutes (K60)
Teach Computers to Learn: Train machine learning models, apply different learning methods, and develop applications that automatically recognize patterns in data.
Contents
- Optimizing techniques
- Evolutionary algorithms
- Reinforcement Learning
- Neural Networks
Competencies
Students are able to
- apply basic optimizing strategies
- explain Genetic Algorithms,
- implement and understand Genetic Programming,
- apply Reinforcement Learning,
- explain artificial Neural Networks
Literature
- Fischer, Jörn: Maschinelles Lernen für Dummies, Wiley CH, ISBN: 978-3527720552, 2024
- I. Goodfellow, Y.Bengio, A. Courville : Deep Learning, MIT Press, ISBN: 978-0262035613, 2016
- Russel, Stuard; Norvig, Peter: Künstliche Intelligenz. Prentice Hall, New Jersey, 1995
- Mitchell, Tom: Machine Learning. McGraw-Hill, 1997
- Zell, Andreas: Simulation Neuronaler Netze. Oldenbourg Verlag, München, 1997
- Sutton, Richard; Barto, Andrew G.: Reinforcement Learning. MIT Press, 1998
Lecturer
Recommended Previous Knowledge
Module Details
| Semester |
6/7 |
| Lecture Language |
German |
|
Frequency
|
Winter Term
|
| Credit Points (ECTS)
|
5 |
| Course Coordinator |
Prof. Dr. Jörn Fischer |
| Duration |
1 Semester |
|
Course Achievement
|
None |
|
Prerequisite for exam
|
Project (PA) |
|
Exam
|
Written Exam 60 Minutes (K60) |
Weekly Hours (SWS)
| Lecture |
2 SWS |
| Exercises |
2 SWS |
| Sum |
4 SWS |
Work Load
| Lecture |
30 h |
| Self Study |
90 h |
| Assignments |
30 h |
| Sum |
150 h |
