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
Specific information on 2027 unit availability will be available in August
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
2027 fee information will be available in August.
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
The teaching pattern for ECA Melbourne and Sydney will be advised by your teaching team. |
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| Assessment | Assignment 2 (30%)|Lab Exercises (30%)|Assignment 1 (40%) |
| Timetable | View 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. |
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The University reserves the right to amend or remove courses and unit availabilities, as appropriate.