Information on individual educational components (ECTS-Course descriptions) per semester | |
Degree programme: | Master Computer Science |
Type of degree: | FH MasterĀ“s Degree Programme |
Full-time | |
Winter Semester 2023 | |
Course unit title | Reinforcement Learning |
Course unit code | 024913030507 |
Language of instruction | German |
Type of course unit (compulsory, optional) | Elective |
Semester when the course unit is delivered | Winter Semester 2023 |
Teaching hours per week | 2 |
Year of study | 2023 |
Level of course unit (e.g. first, second or third cycle) | Second Cycle (Master) |
Number of ECTS credits allocated | 4 |
Name of lecturer(s) | Sebastian HEGENBART |
Prerequisites and co-requisites |
Basics in machine learning, deep-learning, statistics, probability theory and linear algebra. |
Course content |
Reinforcement Learning is an area of machine learning concerned with how "intelligent agents" can learn a strategy to reach a certain goal. In recent years, such agents (based on deep-learning) were able to beat human top players in Go, Starcraft II and Dota in a fair setting. This achievement was thought to be impossible only a few years ago. Methods from reinforcement learning however can not only be used in a game setting but provide various different possible applications from cooperative robots to autonomous vehicles. In this course the principles of reinforcement learning are covered:
Examples and projects will be implemented in python using frameworks such as scikit-learn, numpy and keras (tensorflow).
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Learning outcomes |
The students are able to
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Planned learning activities and teaching methods |
Lectures and mini-projects in groups. |
Assessment methods and criteria |
Written Exam: 75% (must be positive) Excersises: 25% (must be positive) For a positive grade, a minimum of 50% of the possible points must be achieved in each part of the examination. |
Comment |
None |
Recommended or required reading |
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Mode of delivery (face-to-face, distance learning) |
Face-to-Face with selected online elements. |
Winter Semester 2023 | go Top |