Machine Learning
Subject 433-484 (2008)
Note: This is an archived Handbook entry from 2008.Search for this in the current handbook
| Credit Points: | 12.500 | ||||||||||||
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| Level: | Undergraduate | ||||||||||||
| Dates & Locations: | This subject has the following teaching availabilities in 2008: Semester 2, - Taught on campus.
Timetable can be viewed here. For information about these dates, click here. | ||||||||||||
| Time Commitment: | Contact Hours: Twenty-four hours of lectures, 11 hours of workshops Total Time Commitment: Not available | ||||||||||||
| Prerequisites: | 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. | ||||||||||||
| 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: http://services.unimelb.edu.au/disability |
| Subject Overview: | 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. |
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| Assessment: | Project work expected to take about 36 hours during semester (50%), and a 3-hour end of semester written examination (50%). |
| Prescribed Texts: | None |
| Recommended Texts: | Information Not Available |
| Breadth Options: | This subject is not available as a breadth subject. |
| Fees Information: | Subject EFTSL, Level, Discipline & Census Date |
| Generic Skills: | Information Not Available |
| Notes: | Credit may not be gained for both 433-484 Machine Learning and 433-684 Machine Learning. |
| Related Course(s): |
Bachelor of Computer Science (Honours) Bachelor of Engineering (Computer Engineering) Bachelor of Engineering (Electrical Engineering) Bachelor of Engineering (Software Engineering) |
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