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Unit (2020)

Information on this page, including unit offerings, is from the 2020 academic year.

Artificial Intelligence (ICT619)

Organisational Unit Information Technology, Mathematics and Statistics
Credit Points 3
Availability MURDOCH: S1-internal
Teaching Timetables Murdoch S1
Description An overview of the application and associated theories of Artificial Intelligence (AI), machine learning, and techniques used in developing intelligent systems. Emphasis is on data science, business, and engineering problems. Upon completion, students should be able to evaluate machine learning techniques and manage the application of various tools available for developing such systems to solve problems in the real world. Topics included: rule-based systems, artificial neural networks (including deep learning), fuzzy systems, genetic algorithms, data mining, intelligent agents and optimisation techniques.
Unit Learning Outcomes On successful completion of the unit a student should be able to:
ULO1 provide rationale behind the artificial intelligence and machine learning paradigms, with their advantages over traditional computing approaches in solving real world (including data science) problems
ULO2 explain the theoretical foundations of various types of machine learning technologies for specific applications with focus on data intensive applications
ULO3 evaluate AI or machine learning systems, and in particular, their suitability and shortcomings, for specific applications
ULO4 demonstrate and discuss the use of AI or machine learning tools available for developing intelligent systems or data analytics systems.
Timetabled Learning Activities Lectures/workshops: 3 hours per week.
Unit Learning Experiences This unit mainly consists of workshop sessions. Workshops will be covering topics in the unit. There is also an assignment and a project that the students need to hand in over the period of the semester. The purpose of the assignment and project is to demonstrate a larger subset of the learning outcomes and to ensure that students can integrate the knowledge that they have acquired. All the materials and resources for students to complete the assignment and project will be available through LMS.
Other Learning Experiences During the weekly workshop sessions, there will be demonstration of the use of tools, and discussions of problems and their solutions for specific applications.
Assessment Assessment is based on:
(1) a proposal of an intelligent technique based application. The student has to conduct independent research to identify the problem, propose a solution and present the proposal. (15%)
(2) The student has to submit a report together with results of testing, demonstrating how to address the nominated problem. The student has to source the specific tools or programs for this assignment. (35%)
(3) A closed-book end-of-Semester Examination (50%)
Prerequisites Enrolment in a graduate-level IT course.
Exclusions Students who have completed ICT619 Intelligent Systems Applications may not enrol in this unit for credit.
Appears in these Courses/Majors:
see individual structures for context
Data Science (ME+MIT)
Graduate Diploma in Data Science (GradDipDataSci)
Graduate Diploma in Information Technology [New in 2020]
Graduate Diploma in Internetworking and Security (GradDipIntwkSecur)
Master of Business Administration + Master of Information Technology (Data Science) (MBA)+(MIT) [New in 2019]
Master of Information Technology (MIT)
Master of Science in Information Technology (MScIT) [New in 2020]
Doctor of Information Technology (DIT)
Internet Access RequirementsMurdoch units normally include an online component comprising materials, discussions, lecture recordings and assessment activities. All students, regardless of their location or mode of study, need to have access to and be able to use computing devices with browsing capability and a connection to the Internet via Broadband (Cable, ADSL or Mobile) or Wireless. The Internet connection should be readily available and allow large amounts of data to be streamed or downloaded (approximately 100MB per lecture recording). Students also need to be able to enter into online discussions and submit assignments online.


Unit Coordinator
Associate Professor Kevin Wong
Associate Professor

Murdoch Campus
t: 9360 6100
e: K.Wong@murdoch.edu.au
o: 245.1.025 - Science and Computing, Murdoch Campus
Unit Contacts

MURDOCH: S1-Internal
Associate Professor Kevin Wong
Associate Professor

Murdoch Campus
t: 9360 6100
e: K.Wong@murdoch.edu.au
o: 245.1.025 - Science and Computing, Murdoch Campus
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