This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
|Instructor:||Professor Roger D. Peng, PhD|
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