Artificial Intelligence and Machine Learning

Abstract

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.

Syllabus

Introduction to Artificial Intelligence (AI) and Machine Learning (ML)Mathematical foundations for AI and MLLogic in AI/MLUnderstanding the concept of "learning" in MLThe role of Neural Networks in AI/MLDesign and development of AI/ML applicationsProblem solving with AI/ML using examples

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.

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.

Enrolment restrictions

Only available to postgraduate students