| Detailed information |
| Original study plan |
Bachelor's programme Computer Science 2021S |
| Objectives |
Students know the basic concepts underlying and defining the field of Artificial Intelligence (AI), including its history, fundamental goals and assumptions, general approach to problem modeling, and selected algorithms; they will have a broad overview of the field that permits them to pursue more in-depth studies, via more advanced classes or self-study, and to critically evaluate the potential benefits and dangers of AI.
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| Subject |
Definitions of AI. 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 about probabilities. Basics of computer perception.
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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. (2004). Artificial Intelligence: A Modern Approach (3rd Edition). Englewood Cliffs, NJ: Prentice-Hall.
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| Changing subject? |
No |
| Corresponding lecture |
(*)ist gemeinsam mit INBIPUEAINT: UE Artificial Intelligence (1,5 ECTS)
äquivalent zu
INMWAKVKINT: KV Künstliche Intelligenz (3 ECTS)
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