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SIT215 - Artificial and Computational Intelligence

Year:	2021 unit information
Important Update:	Unit delivery will continue to be provided in line with the most current COVIDSafe health guidelines. This may include a mix of on-campus and online activities. To find out how you are impacted, please check your unit sites for announcements and updates. Unit sites open one week prior to the start of each Trimester/Semester. Thank you for your flexibility and commitment to studying with Deakin in 2021. _{Last updated: 4 June 2021}
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 class per week, 1 x 3 hour seminar per week.
Scheduled learning activities - cloud:	1 x 1 hour scheduled online workshop per week.

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	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.