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Machine Learning and Data Mining - COMP9417
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Campus: Kensington Campus
 
 
Career: Postgraduate
 
 
Units of Credit: 6
 
 
EFTSL: 0.12500 (more info)
 
 
Indicative Contact Hours per Week: 3
 
 
Enrolment Requirements:
 
 
Prerequisite: COMP9024.
 
 
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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