This is an elective unit in BCS, BSE, BCS/BA, BCS/LLB, BSc/BCS, and in a computer science major sequence in BSc. It was introduced as part of the 2006 Faculty restructure and will be part of the revised degree roll out of level 2 units for 2007. Previously these degrees had an elective unit CSE2309/3309. FIT3080 is a close mapping of that elective unit, with slight changes due to changes in the pre-requisite units and other core units in the degrees.

This unit has been updated as part of a 2010 strategic course amendment to BCS.

22/06/2016 - Update to learning outcomes for AQF compliance.

31 March 2017: Justification for 3 hour exam added in Assessment summary as approval is required by Faculty Education Committee with new University Examination Procedures. Effective S2 2017.

Students' feedback and reduced engagement requires additional tutorial time.

20/9/2019: Admin - updating exam duration to include additional 10 minutes as per University requirement.

24 Jun 2020: revised assessment regime to suit online/face to face hybrid learning and in response to SETU feedback from previous offering. Renamed laboratory to tutorials to match terminology used in semester

24 Jun 2020: Two changes for S2 2020:

1. Unit name change to make it clearer to students to know what they would expect in the unit, with a name that is modern and resonates.
2. Add extra math prerequisite to emphasize need to know basic probability concepts and necessary math skills.

02/10/2020 Admin: Update to include new assessment and teaching approach fields as per Handbook requirements.

01/07/2021: Admin - Updating for CE - change to learning outcome 8: The reason for this change is so as not to unnecessarily restrict - and to correct a minor typo. Change to assessment: change from 2020's weightings and revert to 2019's - Examination: 60%; In-semester assessment: 40%. The reason for this change is that we both (Hamid and I) feel that this is a better weighting for assessment and a 3 hour exam.

15/07/2021: Matt - Update changes on behalf of the CE and co-lecturer. Rename assignment 2 from 'Multi-agent decision making' to 'Agent decision making'.

This is a specified elective unit introducing artificial intelligence techniques to Bachelor of Computer Science (BCompSci) and Bachelor of Software Engineering (BSE). Other students who meet the appropriate prerequisite knowledge and skills are also able to take this unit.

This unit replaces CSE2309/CSE3309 Artificial Intelligence.

This unit presents the area of Intelligent Systems, one of the 14 main body of knowledge topics identified by the IEEE-ACM Computer Science Curriculum Report 2001. As such it is an essential offering in an undergraduate degree in computer science.

At the completion of this unit students should be able to:

1. Describe the historical and conceptual development of AI; foundational issues for AI, including the frame problem and the Turing test;
2. Explain, apply and evaluate the goals of AI and the main paradigms for achieving them including logical inference, search, machine learning and Bayesian inference;
3. Explain the social and economic roles of AI;
4. Describe, analyze, apply and evaluate heuristic AI for problem solving;
5. Describe, analyze and apply basic knowledge representation and reasoning mechanisms;
6. Describe, analyze and apply probabilistic inference mechanisms for reasoning under uncertainty;
7. Describe, analyze, apply and evaluate machine learning techniques;
8. Describe, analyse, apply and evaluate the use of the above techniques in different domains.

020119

This unit includes history of artificial intelligence; intelligent agents; problem solving and search (problem representation, heuristic search, iterative improvement, game playing); knowledge representation and reasoning (extension of material on propositional and first-order logic for artificial intelligence applications, planning, frames and semantic networks); reasoning under uncertainty (belief networks); machine learning (decision trees, Naive Bayes, neural nets and genetic algorithms); language technology.

Technological requirements

Software: Netica, Weka

On-campus

Lecture and tutorials or problem classes This teaching and learning approach provides lecture and/or tutorials or problem classes, facilitated learning, practical exploration and peer learning.

Examination (3 hours and 10 minutes): 60%; In-semester assessment: 40%

A 3 hour exam is required in order to: (1) authenticate students' knowledge of the material represented by 40% of the assessment. (2) ask in-depth and sufficiently detailed questions which are commensurate with the 40% assessment. (3) give adequate representation to the different topics covered in the subject.

1. Assignment 1: Problem solving through Search 14% - ULO 2, 4, 8
2. Assignment 2: Agent decision making 24% - ULO 2, 5, 6, 7, 8
3. Quizzes 2% - ULO 8
4. Examination 60% - ULO 1, 2, 3, 4, 5, 6, 7, 8

Minimum total expected workload equals 12 hours per week comprising:

(a.) Contact hours for on-campus students:

(b.) Additional requirements (all students):

FIT

(CSE2304 OR FIT2004) AND MAT1830 AND (ONE OF MAT1841, MAT2003, MTH1030, MTH1035, ENG1005)