Data Mining and Minimum Message Length (MML), cse454

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and the

 KL dist
Structured H':
 Decision trees
 Bayes nets


The module examines the intellectual framework of inductive inference, learning and prediction, data modelling, data mining, and the mathematical and algorithmic methods required for unsupervised classification (also called clustering), for supervised classification (e.g. using decision structures) and for other similar and related problems. It is about the mathematics behind the software, the algorithms implemented in the software, and how to write new software for inductive inference. MML is used to combat the overfitting problem and to balance model complexity v. the fit to the data.

2005: Data Mining and MML (3pts)
weeks 2-12, semester 1, [lectures etc.] (L2 Wednesday 9-11am)
Results summary for those with complete assessment as of 6/2005: 71% HD, 14% D, 14% C.
2004: Data Mining and MML (3pts) -- name shortened.
weeks 1-12, semester 1, [lectures etc.] (L4 Monday 12.00-2.00pm)
2003 Learning and Prediction I: Data Mining (3pts):
weeks 1-13, semester 1, [plan] [lectures] & prac's. (E3 Tuesday 3pm)
2002: weeks 1-6, semester 1, [plan] [lectures] & prac's.
CSE454 `Learning and Prediction   I: Data Mining' (3pts) and CSE455 `Learning and Prediction II: MML Data Mining' (3pts) together replaced CSC423 `Learning and Prediction' (6pts).
See [CSE423] (6pts) 1999-2001.
Coding Ockham's Razor, L. Allison, Springer

A Practical Introduction to Denotational Semantics, L. Allison, CUP

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© L. Allison   (or as otherwise indicated),
Faculty of Information Technology (Clayton), Monash University, Australia 3800 (6/'05 was School of Computer Science and Software Engineering, Fac. Info. Tech., Monash University,
was Department of Computer Science, Fac. Comp. & Info. Tech., '89 was Department of Computer Science, Fac. Sci., '68-'71 was Department of Information Science, Fac. Sci.)
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