ITC576 Artificial Intelligence and Machine Learning (8)

This subject provides students with an in-depth study of the issues surrounding the development of Artificial Intelligence (AI) and Machine Learning (ML) systems. It will examine the basic mathematical foundations and logic requirements for developing AI and ML systems. AI and ML will be examined in detail together with the need for data analysis, data matching, decision trees, and Bayesian networks. The role of Neural networks in AI and ML will be considered. The subject will provide students with design and development experience in AI and ML programming and will include a practical section using AI and ML techniques.


Session 2 (60)
Wagga Wagga Campus

Continuing students should consult the SAL for current offering details: ITC576. Where differences exist between the Handbook and the SAL, the SAL should be taken as containing the correct subject offering details.

Subject Information

Grading System



One session


School of Computing and Mathematics

Enrolment Restrictions

Only available to postgraduate students

Assumed Knowledge

Basic understanding of programming is required to complete this subject successfully. Students without programming knowledge are encouraged to enrol in ITC558 prior to attempting this subject.

Learning Outcomes

Upon successful completion of this subject, students should:
  • be able to perform the basic mathematical and logic calculations for Artificial Intelligence and Machine Learning systems;
  • be able to explain the role of several techniques including Bayesian and Neural networks in Artificial Intelligence and Machine Learning systems;
  • be able to critically evaluate the preparation and processing of data for analysis and data matching;
  • be able to critically analyse a proposed AI/ML development proposal; and
  • be able to design and develop an AI/ML application.


This subject will cover the following topics:
  • Introduction to Artificial Intelligence (AI) and Machine Learning (ML)
  • Mathematical foundations for AI and ML
  • Logic in AI/ML
  • Understanding the concept of "learning" in ML
  • The role of Neural Networks in AI/ML
  • Design and development of AI/ML applications
  • Problem solving with AI/ML using examples

The information contained in the CSU Handbook was accurate at the date of publication: May 2021. The University reserves the right to vary the information at any time without notice.