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Machine Learning and Applications unit (KIT315)

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Unit year 2026

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Location: Melbourne Study Centre, Ultimo Study Centre, Hobart, Launceston

Introduction

The aim of this unit is to provide students with the foundation knowledge and understanding of Machine Learning and its applications in various domains including computer vision, data analytics and text mining. This unit will equip students with essential knowledge that is needed for developing smart software applications by using machine learning algorithms and tools.

Summary

Unit name Machine Learning and Applications
Unit code KIT315
Credit points 12.5
College/School Sciences and Engineering
School of Information and Communication Technology
Discipline Information & Communication Technology
Coordinator Doctor Wenli Yang
Available as an elective? Yes
Delivered By University of Tasmania and Third Party(ies): ECA
Level Advanced

Availability

Location Study period Attendance options Available to
Melbourne Study Centre Semester 2 This unit is available through on-campus delivery Available to International Students
Ultimo Study Centre Semester 2 This unit is available through on-campus delivery Available to International Students
Hobart Semester 2 This unit is available through on-campus delivery Available to International Students Available to Domestic Students
Launceston Semester 2 This unit is available through on-campus delivery Available to International Students Available to Domestic Students
  • Key:
  • This unit is available through on-campus delivery On-campus
  • This unit is available through off-campus delivery Off-Campus
  • Available to International Students International students
  • Available to Domestic Students Domestic students
Note

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Units are offered in attending mode unless otherwise indicated (that is attendance is required at the campus identified). A unit identified as offered by distance, that is there is no requirement for attendance, is identified with a nominal enrolment campus. A unit offered to both attending students and by distance from the same campus is identified as having both modes of study.

Key Dates

Study Period Start date Census date WW date End date
Semester 2 5/7/2026 27/7/2026 30/8/2026 24/10/2026

* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (refer to How do I withdraw from a unit? for more information).

Unit census dates currently displaying for 2026 are indicative and subject to change. Finalised census dates for 2026 will be available from the 1st October 2025. Note census date cutoff is 11.59pm AEST (AEDT during October to March).

About Census Dates

Learning Outcomes

  • Explain concepts of different categories of machine learning methods
  • Apply suitable tools and techniques to develop machine learning methods to solve practical problems.
  • Evaluate machine learning solutions toward characteristics of practical problems

Fee Information

Field of Education Commencing Student Contribution 1,3 Grandfathered Student Contribution 1,3 Approved Pathway Course Student Contribution 2,3 Domestic Full Fee 4
029999 $1,192.00 $1,192.00 not applicable $2,919.00
  • Available as a Commonwealth Supported Place
  • HECS-HELP is available on this unit, depending on your eligibility3
  • FEE-HELP is available on this unit, depending on your eligibility4

1 Please refer to more information on student contribution amounts.
2 Please refer to more information on eligibility and Approved Pathway courses.
3 Please refer to more information on eligibility for HECS-HELP.
4 Please refer to more information on eligibility for FEE-HELP.

If you have any questions in relation to the fees, please contact UniConnect or more information is available on StudyAssist.

Please note: international students should refer to What is an indicative Fee? to get an indicative course cost.

Requisites

Prerequisites

KIT205 OR KIT206 OR (for P3R students only: KIT101)

Teaching

Teaching Pattern

On-Campus enrolments in Hobart and Launceston:

Self-Study (on-line): 1hr/week

Workshop (on-line): 2hr/week
Tutorials (on computer lab): 2 hr/week (From week 2). For this unit, students are expected to attend on-campus lab classes.

 

The teaching pattern for ECA Melbourne and Sydney will be advised by your teaching team.

AssessmentAssignment 2 (30%)|Lab Exercises (30%)|Assignment 1 (40%)
TimetableView the lecture timetable | View the full unit timetable

Textbooks

Required

Required readings will be listed in the unit outline prior to the start of classes.

The University reserves the right to amend or remove courses and unit availabilities, as appropriate.