DATA 252 Models and Machine Learning (4)
This project based course provides an overview of modern approaches to analyzing large and complex real world data sets from diverse applications. Students will learn techniques in modeling and predictive methods from selected topics in supervised learning and unsupervised learning. Building off a strong foundation from the generalized linear model framework, students will learn to assess model assumptions and motivate machine learning methods; which may include classification (logistic regression, linear discriminant analysis, naive Bayes, k-means, etc), non-linear and non-parametric methods, support vector machines, decision trees (classification and regression trees, random forests), boosting, neural networks, and additional topics, if time allows. Students will become proficient in implementing these methods using R packages.
- Prerequisite: MATH 280
- Offering: Annually
- Professor: Staff