Introduction to Machine Learning.

Catalog description

This course introduces the concepts of machine learning at the first-semester graduate level. The course begins with a brief review of linear algebra with applications to data manipulation. Next, linear and logistic regression, SVMs, classification, and clustering are reviewed. Data wrangling methods and concepts such as imputation, transformation, and dimensional reduction are discussed, followed by an introduction to model validation. The last third of the course introduces modern machine learning models and concepts: neural networks, deep learning, decision trees, and natural language processing. Prerequisites: Graduate standing or permission of the MS Data Science program director. Corequisite: ANLT 251 Data Science Socratic Lab.