Department of Computational Perception

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Computational Perception

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Artificial Intelligence

344.014, VO, 2h (3 ECTS), WS 2015/16

Univ.-Prof. Dr. Gerhard Widmer

Time: Monday, 12:00 - 13:30
Start: Monday, Oct. 5, 2015
Location: HS 19 (Informatics Building - Science Park 3)

This class will be taught in English.

Class Objectives and Content

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: reinforcement learning
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 (344.021, 344.022, 344.023), 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.

Gerhard Widmer
Tel. 0732 2468 4701

last edited by gw on Aug 17, 2015