Machine Learning

Subject 433-684 (2008)

Note: This is an archived Handbook entry from 2008.Search for this in the current handbook

Credit Points: 12.500
Level: Graduate/Postgraduate
Dates & Locations:

This subject has the following teaching availabilities in 2008:

Semester 2, - Taught on campus.
Pre-teaching Period Start not applicable
Teaching Period not applicable
Assessment Period End not applicable
Last date to Self-Enrol not applicable
Census Date not applicable
Last date to Withdraw without fail not applicable

Timetable can be viewed here. For information about these dates, click here.
Time Commitment: Contact Hours: 24 hours of lectures, 11 hours of workshops; Non-contact time commitment: 84 hours
Total Time Commitment: Not available
Prerequisites: Previous study in artificial intelligence (433-303 or equivalent) and computer graphics (433-380 or equivalent) would be helpful but is not essential.
Corequisites: None
Recommended Background Knowledge: None
Non Allowed Subjects: None
Core Participation Requirements:

For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.

It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support:

Subject Overview: This subject will provide an introduction to the field of machine learning. Machine learning is the task of unearthing regularities in data, and using these to enhance understanding of general processes or predict future events. Topics to be covered include: association rules, clustering, decision trees, decision rules, instance-based learning, statistical learning, numeric prediction, linear discrimination, weakly supervised classification, discretisation, feature selection and classifier combination.
Assessment: Two projects expected to take approx. 36 hours in total during semester (50%) and one written examination 3-hour examination at the end of the semester (50%). Both forms of assessment must be completed satisfactorily to pass the subject.
Prescribed Texts: None
Breadth Options:

This subject is not available as a breadth subject.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
Generic Skills: On successful completion, students will:
  • have an understanding of a representative selection of web mining techniques in both theoretical and applied contexts
  • be familiar with component technologies used in web-based information delivery
Notes: Credit may not be gained for both 433-484: Machine Learning and 433-684: Machine Learning
Related Course(s): Master of Engineering in Distributed Computing
Master of Information Technology
Master of Information Technology
Master of Software Systems Engineering

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