Note: This is an archived Handbook entry from 2009. Search for this in the current handbook
|Dates & Locations:|| |
This subject is not offered in 2009.
|Time Commitment:||Contact Hours: Twenty-four hours of lectures, 11 hours of workshops |
Total Time Commitment: Not available
Study at the third-year level in at least four of the following areas: artificial intelligence, computer design, database systems, graphics, interactive system design, networks and communications, operating systems, programming languages, software engineering, and theory of computation. Prior study in the areas of database systems and artificial intellengence would be helpful.
|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: http://services.unimelb.edu.au/disability
This subject provides 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 to predict future events.
Topics 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.
|Objectives:||On completion, students should: |
Project work expected to take about 36 hours during semester (50%), and a 3-hour end of semester written examination (50%).
|Breadth Options:|| |
This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
On completion of this subject students should:
Credit may not be gained for both 433-484 Machine Learning and 433-684 Machine Learning.
Bachelor of Computer Science (Honours) |
Bachelor of Engineering (Computer Engineering)
Bachelor of Engineering (Electrical Engineering)
Bachelor of Engineering (Software Engineering)
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