Statistics 652: Course Description

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Statistics 652:

Statistical machine learning overview. Choosing a learning algorithm. Supervised learning including classification methods and predition methods, nearest neighbors, naïve Bayes, decision trees and rules, linear regression and logistic regression, neural networks. Unsupervised learning including: PCA, k-means, market basket analysis. Model performance and evaluation. Confusion matrix. Report writing.

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