Overview

This unit aims to provide students with an in-depth understanding of algorithmic problem-solving techniques and the sophisticated concepts of artificial intelligence employed to tackle intricate problems. It presupposes that students possess proficient programming skills in at least one of the following languages: Python, Java, C#, or C++.

Requisites

Prerequisites
COS20007 Object Oriented Programming

OR
COS30008 Data Structures and Patterns

Assumed Knowledge
Object oriented programming at an intermediate level

Teaching periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date
Teaching Period 3
Location
Online
Start and end dates
04-November-2024
09-February-2025
Last self-enrolment date
17-November-2024
Census date
29-November-2024
Last withdraw without fail date
27-December-2024
Results released date
04-March-2025

Learning outcomes

Students who successfully complete this unit will be able to:

  • Describe and interpret the fundamental concepts of Artificial Intelligence (AI) and generic problem solving techniques
  • Apply advanced algorithms and data structures to solve common problems
  • Design software that implements AI concepts

Teaching methods

Hawthorn

Type Hours per week Number of weeks Total (number of hours)
Live Online
Lecture		2.00 8 weeks 16
On-campus
Lecture		2.00 4 weeks 8
On-campus
Class		2.00 12 weeks 24
Unspecified Activities
Independent Learning		8.50 12 weeks 102
TOTAL150

All Applicable Locations

Type Hours per week Number of weeks Total (number of hours)
Online
Directed Online Learning and Independent Learning		12.50 12 weeks 150
TOTAL150

Assessment

Type Task Weighting ULO's
Assignment 1Individual 20 - 30% 2,3 
Assignment 2Group 20 - 30% 2,3 
Final-Semester TestIndividual 20 - 30% 1,2 
Mid-Semester TestIndividual 20 - 30% 1,2 

Content

  • Introduction to Artificial Intelligence and Intelligent Agents
  • Introduction to Logic and Reasoning
  • Uninformed and Informed Search
  • Knowledge Representation
  • Expert Systems
  • AI Planning
  • Uncertain Knowledge and Reasoning
  • Decision Making with Uncertainty
  • Adaptation and Machine Learning
  • Philosophical Aspects of AI

Study resources

Reading materials

A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.