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This class is usually taught by Gerhard Widmer in the winter term. The class is taught in English.
Information for the current semester (if available):
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Goals and Contents of this Class
The lecture gives an introduction to basic models and techniques in the field of Artificial Intelligence (AI). Specific topics to be covered include:
- Definitions of AI, history of AI, current state of the field
- Motivating scenario: autonomous intelligent "agents"
- Problem solving as a search process:
- uninformed search algorithms
- heuristic search algorithms
- heuristic search in game playing
- Knowledge representation and logical inference:
- Propositional logic
- First-order (predicate) logic
- Logic as a programming language: PROLOG
- Representing and reasoning with uncertain knowledge:
- Basics of Bayesian probability theory
- Knowledge representation and inference in Bayesian networks
- Basic notions of machine learning:
- Learning logical definitions: inductive concept learning
- Learning strategies for intelligent action selection: reinforcement learning
- Learning about probabilities: logistic regression and neural networks
- Basic notions of computer perception
Teaching materials:
PDF versions of the Powerpoint slides used in the lecture will be made available via KUSSS (weekly).
Recommended reading (will not be needed if the lectures are attended on a regular basis):
Russell, S.J. and Norvig, P. (2000). Artificial Intelligence: A Modern Approach (2nd. Edition). Englewood Cliffs, NJ: Prentice-Hall.
Exercise track (Übung)
The class is accompanied by an exercise track, in which the students will improve their understanding of the material by solving a series of theoretical and practical examples.
Be sure to also register for one of 344.021, 344.022, 344.023!
Questions, suggestions, complaints, etc. to:
Gerhard Widmer, opens an external URL in a new window
Tel. 2468-4701
gerhard dot widmer at jku dot at
