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Machine Learning and Data Mining - COMP9417
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Campus: Kensington Campus
 
 
Career: Undergraduate
 
 
Units of Credit: 6
 
 
EFTSL: 0.12500 (more info)
 
 
Indicative Contact Hours per Week: 3
 
 
Enrolment Requirements:
 
 
Prerequisite: COMP2011 or COMP2711 or COMP2911.
 
 
Fee Band: 2 (more info)
 
 
Further Information: See Class Timetable
 
  

Description

Machine learning is the algorithmic approach to learning from data. This course covers the key techniques in data mining technology, gives their theoretical background and shows their application. Topics include: decision tree algorithms (such as C4.5), regression and model tree algorithms, neural network learning, rule learning (such as association rules), lazy learning, version spaces, evaluating the performance of machine learning algorithms, Bayesian learning and model selection, algorithm-independent learning, ensemble learning, kernel methods, unsupervised learning (such as clustering) and inductive logic programming (relational learning)

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© The University of New South Wales (CRICOS Provider No.: 00098G), 2004-2011. The information contained in this Handbook is indicative only. While every effort is made to keep this information up-to-date, the University reserves the right to discontinue or vary arrangements, programs and courses at any time without notice and at its discretion. While the University will try to avoid or minimise any inconvenience, changes may also be made to programs, courses and staff after enrolment. The University may also set limits on the number of students in a course.