DATA 601 - Applied Machine Learning

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This course will focus on the technical application of machine learning algorithms, their nature, and discussions regarding the potential drawbacks and advantages of different classes of algorithms. Students entering into this course should have, at a minimum, a background in python, linear algebra, and vector calculus. No single algorithm will be covered in great depth, and the course will place a focus on the code and implementation choices necessary for each class of algorithm. Topics covered will include introductory data engineering, linear regression, decision trees, forests, k-nn, support vector machines, kernel SVM, naive bayes, k-means and hierarchical clustering, association rules, natural language processing, neural networks, and dimensionality reduction strategies. Technically, you will learn how to distribute these methods in the William & Mary HPC environment.