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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

S1-FF-CLAYTON-FLEXIBLE

S2-01-CLAYTON-ON-CAMPUS

Rules

Enrolment Rule

Contacts

Chief Examiner(s)

Associate Professor Chung-Hsing Yeh

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:
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.

Assessment summary

Examination (2 hours and 10 minutes): 60%; In-semester assessment: 40%

This unit contains hurdle requirements which you must achieve to be able to pass the unit. The consequence of not achieving a hurdle requirement is a fail grade (NH) and a maximum mark of 45 for the unit.

Scheduled and non-scheduled teaching activities

Laboratories

Lectures

Workload requirements

Workload