UnitLevel 3Undergraduate

FIT3080 Artificial intelligence

Faculty of Information Technology

FIT3080 Artificial intelligence is a level 3, 6-credit-point, undergraduate unit from the Faculty of Information Technology, offered in 2022 in Semester 2 at Clayton and Malaysia. It has no prerequisites.

Credit points
6
Offered in 2022
Semester 2
Clayton, Malaysia
Assessment
Exam 60%
and 3 other tasks
Workload
144 hours
per semester

This is the 2022 handbook entry. See the 2027 entry.

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Requisites

Before FIT3080

No prerequisites or corequisites besides the enrolment rules below.

After FIT3080

No unit lists FIT3080 as a prerequisite in the 2022 handbook.

Enrolment rules

Prerequisites: FIT2004 and MAT1830 and one of (MAT1841, MTH1030, MTH1035, ENG1005)

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 trees, Naive Bayes, neural nets and genetic algorithms); language technology.

Offerings in 2022

Teaching periodCampusMode
Second semesterClaytonOn campus
Second semesterMalaysiaOn campus

Assessment

  • Assignment 1: Problem solving through SearchAssignmentThreshold hurdle
    14%
  • Assignment 2: Agent decision makingAssignmentThreshold hurdle
    24%
  • QuizzesOtherThreshold hurdle
    2%
  • Scheduled final assessment (3 hours and 10 minutes)ExamThreshold hurdle
    60%

Learning outcomes

When you finish this unit, you should be able to:

  1. 1

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

  2. 2

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

  3. 3

    Explain the social and economic roles of AI;

  4. 4

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

  5. 5

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

  6. 6

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

  7. 7

    Describe, analyse, apply and evaluate machine learning techniques;

  8. 8

    Describe, analyse, apply and evaluate the use of the above techniques in different domains.

Workload and teaching

  • Lectures24 hours
  • Practical activities22 hours
  • Teaching approachActive learning

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

Software: Netica, Weka

Where it fits

FIT3080 is part of 3 areas of study in the 2022 handbook.

Contacts

Unit Coordinators
Associate Professor Ting Chee Ming
Chief Examiners
Dr Daniel Harabor

Common questions

What are the prerequisites for FIT3080?

FIT3080 has no prerequisites, but enrolment rules apply.

When is FIT3080 offered?

In 2022, FIT3080 runs in Semester 2 at Clayton and Malaysia.

How much work is FIT3080?

The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.

Does FIT3080 have an exam?

Yes. The exam is worth 60% of the final mark, alongside 3 other tasks.

Which majors and minors include FIT3080?

FIT3080 is part of Advanced computer science, Computational science and Software engineering.

More details

Credit points
6
Level
3
Study level
Undergraduate
Faculty
Faculty of Information Technology
Type
Coursework
EFTSL
0.125
Student contribution
SCA Band 2
Study abroad
Available