DATA 510 - Advanced Applied Machine Learning

(3) Runfola.

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 and linear algebra. The course will place a focus on the code and implementation choices necessary for each class of algorithm, especially in the context of applied optimization challenges. Topics covered will include data processing, regression in ML, 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.