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Overview

This unit introduces the main problems and approaches to designing intelligent software systems including automated search methods, knowledge representation and reasoning, planning, reasoning under uncertainty, machine learning paradigms, and evolutionary algorithms.

Offerings

S1-01-CLAYTON-ON-CAMPUS

S1-01-OS-CHI-SEU-ON-CAMPUS

S2-01-CLAYTON-ON-CAMPUS

Contacts

Chief Examiner(s)

Dr Julian Gutierrez

Learning outcomes

On successful completion of this unit, you should be able to:
1.

Explain the theoretical foundations of Artificial Intelligence (AI) - such as the Turing test, Rational Agency and the Frame Problem - that underpin the application to information technology and society;

2.

Critically explain, evaluate and apply appropriate AI theories, models and/or techniques in practice - including logical inference, heuristic search, genetic algorithms, supervised and unsupervised machine learning and Bayesian inference;

3.

Utilise appropriate software tools to develop AI models or software;

4.

Utilise and explain evaluation criteria to measure the correctness and/or suitability of models.

Teaching approach

Peer assisted learning

Assessment summary

This unit has threshold mark hurdles. You must achieve at least 45% of the available marks in the final scheduled assessment, at least 45% in total for in-semester assessments, and an overall unit mark of 50% or more to be able to pass the unit. If you do not achieve the threshold mark, you will receive a fail grade (NH) and a maximum mark of 45 for the unit.

Assessment

1 - Assessment Task 1

2 - Assessment Task 2

3 - Scheduled final assessment (2 hours and 10 minutes)

Scheduled and non-scheduled teaching activities

Laboratories

Lectures

Workload requirements

Workload

Learning resources

Required resources

Technology resources