This unit might be offered in 2017, subject to enrolment numbers
Introduction
This unit will extend the treatment of two or three of the areas of AI introduced in KIT108, adding more depth and having a more technical focus. The specific topics may vary between years, reflecting the expertise of the staff involved in offering the unit in particular years. Students should contact the unit coordinator each year to find out the topics for that year. Possible topics include: Data mining / machine learning (including neural networks), computer vision, genetic algorithms, real-time aspects of AI, knowledge based systems and robotics, AI in Games.
Summary 2020
| 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 | |
| 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.
Special approval is required for enrolment into TNE Program units.
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).
Fees
Requisites
KIT108 and DN/HD in KIT205 or KIT206 or KIT305 or KIT307
Prerequisites
Co-requisites
Mutual Exclusions
You cannot enrol in this unit as well as the following:
- KIT415
Teaching
| Teaching Pattern | Three weeks of intensive classes. |
|---|---|
| Assessment | 100% in-semester (3 assignments 30%, 30%, 40%) |
| Timetable | View the lecture timetable | View the full unit timetable |
Textbooks
| Required | |
|---|---|
| Recommended |
The University reserves the right to amend or remove courses and unit availabilities, as appropriate.