CI6124 Data Mining & Machine Learning
The knowledge discovery process; data preparation; Supervised and unsupervised learning. Machine learning: decision tree induction, rule induction, nearest neighbour categorisation, cluster analysis, Bayesian learning and neural networks; Web mining: content, structure and usage mining; Information mining software and tools; Recommender systems and intelligent information retrieval; spiders; semantic Web; information agents and brokers.
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FAQ
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by
Kuiyu Chang
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last modified
Aug 25, 2008 07:23 AM
- Anything about the course
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Books
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by
Kuiyu Chang
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last modified
Aug 25, 2008 07:23 AM
- You should consider getting at least the text. The references are listed in ascending order of difficulty/levels, i.e. first book is introductory, last book is the hardest.
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Lecture Slides
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by
Kuiyu Chang
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last modified
Aug 25, 2008 07:23 AM
- The following lecture slides are derived primarily from Tan/Steinbach/Kumar's slides available @ http://www-users.cs.umn.edu/~kumar/dmbook/index.php, and from other secondary sources.
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Syllabus
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by
Kuiyu Chang
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last modified
Aug 25, 2008 07:24 AM
- Tentative, subject to change.
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News
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Kuiyu Chang
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last modified
Aug 25, 2008 07:24 AM
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Quiz
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Kuiyu Chang
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last modified
Aug 25, 2008 07:24 AM
- In class quiz (past quizzes downloadable) & results.
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Anonymous Forum
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by
Kuiyu Chang
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last modified
Aug 25, 2008 07:24 AM
- Discuss anything related to the class in the anonymized safety of your home.
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An Overview of Computational Learning and Function Approximation
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by
Kuiyu Chang
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last modified
Aug 25, 2008 07:24 AM
- Scan from my hardcopy Friedman, J. H (1994). "An Overview of Computational Learning and Function Approximation" In: From Statistics to Neural Networks. Theory and Pattern Recognition Applications. (Cherkassy, Friedman, Wechsler, eds.) Springer-Verlag 1
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2006 Exam paper & solutions
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by
Kuiyu Chang
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last modified
Aug 25, 2008 07:24 AM
- Solutions may contain errors, use your discretion.

