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ITC364 Computational Intelligence (8)

Abstract

Computational Intelligence is the study of the design of intelligent agents. This subject develops and links the theoretical and experimental aspects of the discipline. The subject explores the specification and design of intelligent agents, implementing, testing and improving real software systems for many challenging application domains. The application areas include: delivery robots, diagnostic assistants and information slaves. This subject will serve as an introduction to the more specialised Artificial Intelligence topics concerning neural networks, genetic algorithms, expert systems, machine learning and machine vision.

+ Subject Availability Modes and Location

Continuing students should consult the SAL for current offering details prior to contacting their course coordinator: ITC364
Where differences exist between the handbook and the SAL, the SAL should be taken as containing the correct subject offering details.

Subject information

Duration Grading System School:
One sessionHD/FLSchool of Computing and Mathematics

Assumed Knowledge

Mathematical background at the level of MTH129 Programming background at the level of (ITC226 or ITC313 or ITC322)

Enrolment restrictions

Not available to students who have completed ITC528
Incompatible subject(s)Related subject(s)
ITC528 ITC528 Paired Subject

Learning Outcomes

Upon successful completion of this subject, students should:
  • be able to explain the basic components of an intelligent agent;
  • be able to apply the First-Order Logic approach to the fundamentals of computational intelligence;
  • be able to analyse how computational intelligence concepts are employed in various applications;
  • be able to design simple computational intelligence programs, and analyse more complicated ones;
  • be able to analyse the components of an expert system;
  • be able to explain the issues associated with agent learning.

Syllabus

The subject will cover the following topics:
  • Introduction to computational intelligence
  • Problem-solving by search strategies
  • Logical Agents and First-Order Logic
  • Agent action planning
  • Knowledge representation
  • Uncertain knowledge and reasoning
  • The theory of learning and machine learning
  • Probabilistic learning models

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The information contained in the 2016 CSU Handbook was accurate at the date of publication: 06 September 2016. The University reserves the right to vary the information at any time without notice.