DATA 301 - Applied Machine Learning

Credits: (3)
Prerequisite(s): (CSCI 140/CSCI 141/DATA 141) and (CSCI 146/DATA 201)
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. 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 probability, distributions, Monte-Carlo simulations, reinforcement learning, association rules, nonlinear regression, support vector machines, kernel SVM, variable/model selection, diagnostics for regression and classification, neural networks/deep learning, natural language processing, and various associated approaches.
Formerly: DATA 310