Courses & Units

Machine Learning and Applications KIT315


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.


Unit name Machine Learning and Applications
Unit code KIT315
Credit points 12.5
College/School College of 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
Level Advanced


Location Study period Attendance options Available to
Hobart Semester 2 On-Campus International Domestic
Launceston Semester 2 On-Campus International Domestic


International students
Domestic students

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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 22/7/2024 16/8/2024 9/9/2024 27/10/2024

* 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 2024 are indicative and subject to change. Finalised census dates for 2024 will be available from the 1st October 2023. 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

The 2024 Commonwealth Supported Place (CSP) rates are still being finalised by the Government and we will update the domestic fee information as soon as we have more details.



KIT205 OR KIT206


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. For this unit, students are expected to attend on-campus lab classes, an online lab class will be avaiable for special circumstances (permission is required to attend the online tutorial). 

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



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

LinksBooktopia textbook finder

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