ITO5047 · Fundamentals of artificial intelligence

Overview

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

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

In-semester assessment: 100%

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Explain the theoretical foundations of Artificial Intelligence (AI) 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 formal reasoning techniques, heuristic search, neural network based reasoning, supervised and unsupervised machine learning and Bayesian inference;
3. Utilise appropriate software tools to develop AI models or software; and
4. Utilise and explain evaluation criteria to measure the correctness and/or suitability of models.

Workload

A minimum of 144 hours over the 6 week teaching period should be used to complete assignments, participating in discussions, private study and revision.

Student experience, not official rules. Nothing here changes what the Handbook says.