(3 credits)Prerequisites:MATH 2050 or equivalent, or permission of instructor.
Provides a broad but thorough introduction to the methods and practice of statistical machine learning. Techniques covered include nearest neighbor classifiers, random forests, density estimation, basis functions, decision trees, cluster analysis, and support vector machines, with focus on intuitive understanding and applications of these methods, rather than theoretical development.