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Module Descriptor School of Computer Science and Statistics

Module CodeCS3061
Module NameArtificial Intelligence I
Module Short TitleA I
Semester TaughtSemester 2 (Hilary)
Contact Hours

33 (22 lecture, 11 tutorial)

Module PersonnelTim Fernando
Learning Outcomes

After successfully completing this module, students should be able to:

  • Describe the basic aims and achievements of artificial intelligence, as well as the challenges facing it
  • Assess notions of computability as they relate to artificial intelligence
  • Reason about the suitability of and trade-offs between basic search strategies
  • Design simple knowledge representation systems for various knowledge-intensive problems
  • Understand the possibilities opened up by meta-interpretation
  • Appreciate the possibilities opened up by probabilistic reasoning
Learning Aims

An introduction to Artificial Intelligence covering basic topics search and knowledge representation, including an introduction to probabilistic reasoning.

Module Content

Search, constraint satisfaction, knowledge representation, abduction, introduction to probabilistic reasoning

Recommended Reading List

Russell and Norvig, Artificial Intelligence: A Modern Approach

Module Prerequisites


Assessment Details

Written exam (90%) and continuous assessement consisting of lab work and problem sets (10%)
The supplemental assessment will be based solely (i.e. 100%) on the written exam.
Exam duration: 2 hours (annual and supplemental)

Module Website
Academic Year of Data2018/19