SPECIAL NOTE:
unit is weighted 12.5% for postgraduate courses
OFFERINGS
Not Offered
DESCRIPTION
Introduces the key current ideas and techniques in machine learning in sufficient depth to enable students to apply them to practical (data mining) problems and to participate in research in the area. The major focus is on classifier learning and the evaluation of classifier learning techniques. The types of classifiers studied include decision trees, rule sets, instance-based, naive Bayesian, neural networks, and combined methods. Other topics include continuous value prediction and inductive logic programming.
WEIGHT:
12.5%
TEACHING PATTERN: lectures or seminars weekly as advised by the lecturer
FLEXIBLE & ONLINE STUDY OPTIONS Note: Class attendance may still be required
Web dependent -
H,L
Some parts of this unit will be taught online
Video conferencing -
H,L
A live video link between campuses is used for at least some teaching in this unit
About Flexible Study Options
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.
Campus - H Hobart, L Launceston, W Burnie. Study Centre - V Sydney, R Rozelle. Distance units may also have a campus identifier of I Isolated, N Interstate, O Overseas. Units delivered in Transnational Education (TNE) Programs have a campus identifier of A Hangzhou, F Fuzhou, G Shanghai, J Indonesia, K KDU Malaysia, Q Kuwait or Z New Zealand.
Special approval is required for enrolment into TNE Program units - campuses A, F, G, J, K, Q and Z click here for more information.
Web dependent -
H,L
