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SIT215 - Computational Intelligence

Year:

2022 unit information

Important Update:

Unit delivery will be in line with the most current COVIDSafe health guidelines. We continue to tailor learning experiences for each unit to achieve the best possible mix of online and on-campus activities that successfully blend our approaches to learning, working and research. Please check your unit sites for announcements and updates.

Last updated: 4 March 2022

Enrolment modes:Trimester 1: Burwood (Melbourne), Online
Credit point(s):1
EFTSL value:0.125
Unit Chair:Trimester 1: Somaiyeh Mahmoud Zadeh
Prerequisite:

SIT192 and SIT112 OR SIT114

Corequisite:SIT221
Incompatible with:

Nil

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

Scheduled learning activities - cloud:

Online independent and collaborative learning including optional scheduled activities as detailed in the unit site.

Content

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.

ULO These are the Learning Outcomes (ULO) for this unit. At the completion of this unit, successful students can: Deakin Graduate Learning Outcomes
ULO1 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

ULO2

 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

ULO3

 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

Assessment Description Student output Grading and weighting
(% total mark for unit)		 Indicative due week
Problem solving tasks (group) Two problem-based learning tasks 40% (2 x 20%) Weeks 5 and 7
Quiz Open book quiz 20% Week 9
Project Software source code, resource files and written report, approximately 2,500 words, and presentation 40% (20%, 10%, 10%) (Report, Code, Presentation) Week 11

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. 

The texts and reading list for the unit can be found on the University Library via the link below: SIT215 Note: Select the relevant trimester reading list. Please note that a future teaching period's reading list may not be available until a month prior to the start of that teaching period so you may wish to use the relevant trimester's prior year reading list as a guide only.

Unit Fee Information

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