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 2021
| Unit name | Machine Learning and Applications |
|---|---|
| Unit code | KIT315 |
| Credit points | 12.5 |
| Faculty/School | College of Sciences and Engineering School of Information and Communication Technology |
| Discipline | Information & Communication Technology |
| Coordinator | Son Tran |
| Teaching staff | |
| Level | Advanced |
| Available as student elective? | Yes |
| Breadth Unit? | No |
Availability
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.
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TNE Program units special approval requirements.
* 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 (see withdrawal dates explained for more information).
Learning Outcomes
1 | Explain concepts of different categories of machine learning methods |
2 | Apply suitable tools and techniques to develop machine learning methods to solve practical problems. |
3 | Evaluate machine learning solutions toward characteristics of practical problems |
Fees
Requisites
Prerequisites
KIT205 or KIT206
Teaching
| Teaching Pattern | 2-hour lecture weekly, 2-hour tutorial weekly |
|---|---|
| Assessment | AT1 - Assignment 40% AT2 - Report 30% AT3 - Workshop (regular lab exercises) 30% |
| Timetable | View the lecture timetable | View the full unit timetable |
Textbooks
| Required | |
|---|---|
| Recommended |
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