Studying online

There are now 2 possible online modes for units:

Units with modes Online timetabled and Online flexible are available for any student to self-enrol and study online.

Click on an offering mode for more details.

Unit Overview

Description

Building software modules that can learn from, and adapt to, a changing and unknown environment is a key challenge faced in many complex real-world problems. This unit covers a class of nature-inspired algorithms and structures for creating programs that demonstrate emergent adaptive and intelligent behaviours, including evolutionary algorithms, neural networks, machine learning and a swarm intelligence, contrasted against traditional optimisation techniques. The representations and algorithms explored in the unit can be used to solve problems ranging from complex optimisation to adaptive learning, which form the core research areas of artificial intelligence. Numerous research questions remain when such techniques are applied in real-world situations. In this interactive, project-based unit, students are given opportunities to explore the above-mentioned advanced topics in artificial intelligence and adaptive systems, research into a topic or technique of interest and develop and apply software solutions in simulated environments.

Credit
6 points
Offering
(see Timetable)
AvailabilityLocationMode
Semester 1UWA (Perth)Face to face
Outcomes

Students are able to (1) understand the general concepts and approaches used in building AI and adaptive systems; (2) perform a literature search and research investigation on at least one AI approach; (3) apply at least one AI approach to solve significant real-world problems; (4) participate effectively as a member of a team and contribute constructively to team goals; (5) produce scientific writing that explains the hypothesis, experimental design, and evaluation strategy of a problem solution ; and (6) explain AI approaches and their application in seminar settings..

Assessment

Indicative assessments in this unit are as follows: (1) research paper; (2) practical project; and (3) final examination. Further information is available in the unit outline.



Student may be offered supplementary assessment in this unit if they meet the eligibility criteria.

Unit Coordinator(s)
Associate Professor Cara MacNish
Unit rules
Prerequisites
Successful completion of
CITS2002 Systems Programming
or CITS2005 Object Oriented Programming
or CITS2402 Introduction to Data Science
or ELEC3020 Embedded Systems
or ( CITS1401 Computational Thinking with Python
and CITS4009 Computational Data Analysis
)
Advisable prior study
some experience with python,
or willingness to learn, is recommended.
Note
This unit will not be offered in 2021.
  • The availability of units in Semester 1, 2, etc. was correct at the time of publication but may be subject to change.
  • All students are responsible for identifying when they need assistance to improve their academic learning, research, English language and numeracy skills; seeking out the services and resources available to help them; and applying what they learn. Students are encouraged to register for free online support through GETSmart; to help themselves to the extensive range of resources on UWA's STUDYSmarter website; and to participate in WRITESmart and (ma+hs)Smart drop-ins and workshops.
  • Visit the Essential Textbooks website to see if any textbooks are required for this Unit. The website is updated regularly so content may change. Students are recommended to purchase Essential Textbooks, but a limited number of copies of all Essential Textbooks are held in the Library in print, and as an ebook where possible. Recommended readings for the unit can be accessed in Unit Readings directly through the Learning Management System (LMS).
  • Contact hours provide an indication of the type and extent of in-class activities this unit may contain. The total amount of student work (including contact hours, assessment time, and self-study) will approximate 150 hours per 6 credit points.