FIT3080 - Artificial intelligence

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Overview

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 … For more content click the Read More button below.

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.

Offerings

S2-01-CLAYTON-ON-CAMPUS

S2-01-MALAYSIA-ON-CAMPUS

Rules

Enrolment Rule

Contacts

Chief Examiner(s)

Dr Frits de Nijs

Unit Coordinator(s)

Ms Ting Fung

Notes

IMPORTANT NOTICE:
Scheduled teaching activities and/or workload information are subject to change in response to COVID-19, please check your Unit timetable and Unit Moodle site for more details.

Learning outcomes

On successful completion of this unit, you should be able to:

Describe the historical and conceptual development of AI; foundational issues for AI, including the frame problem and the Turing test;

Explain, apply and evaluate the goals of AI and the main paradigms for achieving them including logical inference, search, machine learning and Bayesian inference;

Explain the social and economic roles of AI;

Describe, analyse, apply and evaluate heuristic AI for problem solving;

Describe, analyse and apply basic knowledge representation and reasoning mechanisms;

Describe, analyse and apply probabilistic inference mechanisms for reasoning under uncertainty;

Describe, analyse, apply and evaluate machine learning techniques;

Describe, analyse, apply and evaluate the use of the above techniques in different domain, specifically language technology.

Teaching approach

Active learning

Assessment

1 - In-semester assessment

2 - Examination (3 hours and 10 minutes)

Scheduled and non-scheduled teaching activities

Lectures

Tutorials

Workload requirements

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning and online engagement.

Learning resources

Technology resources

Availability in areas of study

Advanced computer science