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SIT215 - Artificial and Computational Intelligence


2020 unit information

Important Update:

Classes and seminars in Trimester 2/Semester 2, 2020 will be online. Physical distancing for coronavirus (COVID-19) will affect delivery of other learning experiences in this unit. Please check your unit sites for announcements and updates one week prior to the start of your trimester or semester.

Last updated: 2 June 2020

Offering information:

From 2021 - unit will be offered in Trimester 1 

Enrolment modes:Trimester 2: Burwood (Melbourne), Cloud (online)
Credit point(s):1
EFTSL value:0.125
Unit Chair:Trimester 2: Somaiyeh MahmoudZadeh

SIT192 and SIT112 OR SIT114

Incompatible with:


Typical study commitment:

Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit.

Scheduled learning activities - campus:

1 x 1 hour class per week, 1 x 3 hour seminar per week.

Scheduled learning activities - cloud:

1 x 1 hour scheduled online workshop per week.


The development of intelligent systems has been a central quest of computer scientists for more than fifty years, with the objective of creating artificial systems that can solve problems traditionally achievable only by humans. This field is underpinned by a range of computational patterns and methods that cover problems such as searching, problem solving, reasoning, knowledge representation and learning. In this unit students will investigate, through a range of problem-based learning activities, a range of artificial and computational intelligence techniques that underpin modern, advanced intelligent systems such as autonomous vehicles, robotics, game-playing agents, and expert systems.


These are the Learning Outcomes (ULO) for this Unit

At the completion of this Unit
successful students can:

Deakin Graduate Learning Outcomes


Apply specific algorithms and data structures to model a range of problems arising in intelligent systems development GLO1: Discipline-specific knowledge and capabilities
GLO5: Problem solving


Design and implement software artefacts to demonstrate effectiveness and efficiency of solutions for intelligent systems development GLO1: Discipline-specific knowledge and capabilities
GLO4: Critical thinking
GLO7: Teamwork


Apply theoretical concepts and models to explain and communicate the design of intelligent systems GLO1: Discipline-specific knowledge and capabilities
GLO2: Communication
GLO7: Teamwork

These Unit Learning Outcomes are applicable for all teaching periods throughout the year


Assessment Description Student output Weighting (% total mark for unit) Indicative due week
Problem solving tasks (group) Four problem-based learning tasks 40% Weeks, 4, 6, 8 and 10
Project Software source code, resource files and written report, approximately 1,500 words 30% Week 11
Examination 2-hour written examination 30% Examination period

The assessment due weeks provided may change. The Unit Chair will clarify the exact assessment requirements, including the due date, at the start of the teaching period.

Learning Resource

There is no prescribed text. Unit materials are provided via the unit site. This includes unit topic readings and references to further information.