| Detailed information |
| Original study plan |
Bachelor's programme Computer Science 2025W |
| Learning Outcomes |
Competences |
| Students can interpret and critically evaluate new developments in the field of Artificial Intelligence, in terms of both historical and methodological contexts. Based on an understanding of fundamental AI-related concepts they can read new scientific literature on various subareas of AI, can interpret this information in a wider context, and can thus independently improve their knowledge of the field.
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| Skills |
Knowledge |
Students
- know how to formulate and formalise various kinds of problems as search, inference, or machine learning tasks;
- can identify appropriate methods for solving these (k3);
- understand the fundamental assumptions as well as the possibilities and limitations of fundamental classes of AI methods (k2/k5).
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- Definitions of AI
- Fundamental concepts of AI, including underlying assumptions and historical context
- problem solving as a search process: search algorithms (uninformed and heuristic)
- heuristic search in games
- knowledge representation and logical reasoning: inference in propositional logic
- reasoning with uncertain knowledge: knowledge representation and inference in Bayesian nets
- Machine learning: inductive concept learning, reinforcement learning, learning of probabilistic classifiers; basic concepts of neural networks and deep learning.
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| Criteria for evaluation |
Written exam at the end of the semester
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| Methods |
Standard lectures with study materials (slides) provided.
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| Language |
English |
| Study material |
Pdf versions of the presentation slides used in the lecture are made available via KUSSS or Moodle (weekly).
Recommended reading (will not be needed if the lectures are attended on a regular basis):
Russell, S.J. and Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th Edition). Pearson, 2020. ISBN 978-0134610993.
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| Changing subject? |
No |
| Further information |
The lecture series (VL) and the corresponding exercise course (UE) form a didactic unit. The study results described here are achieved through the combination of these two courses.
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| Corresponding lecture |
(*)ist gemeinsam mit INBIPUEAINT: UE Artificial Intelligence (1,5 ECTS)
äquivalent zu
INMWAKVKINT: KV Künstliche Intelligenz (3 ECTS)
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