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Training materials: XL Miner software (trial version); Data Mining Tools student participant guide. Recommended (but not required) texts: The Elements of Statistical Learning by T. Hastie, R. Tibshirani, J. H. Friedman; Principles of Data Mining (Adaptive Computation and Machine Learning) by David J. Hand, Heikki Mannila, Padhraic Smyth.
Course agenda
I. OVERVIEW OF DATA MINING
• Definitions and tasks
• Steps in the data mining process
• Facts versus myths
• Data mining applications
II. PREPATORY STEPS
• Data preparation (missing data, outlier analysis, data types)
• Data visualization
• Data dictionaries
III. BACKGROUND ON MODELING
• The curse of dimensionality
• Notation and terms
• Bias-variance tradeoff
• Control of the bias-variance tradeoff
• Error functions
IV. TRADITIONAL MODELS
• Linear regression procedures
• Logistic regression
• Discriminant analysis
• Nearest neighbors
• Clustering algorithms
V. MODERN MODELS
• Classification and Regression Trees
• Neural networks
• Bump hunting
• Association rules
• Evaluating and combining models (Bagging, boosting, MART)
• Survey of recent developments (as time permits)
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