Research in Information Studies: Discovering the World with Data

Large datasets are increasingly becoming available across many sectors such as healthcare, energy, and online markets. This course focuses on methods that allow “learning” from such datasets to uncover underlying relationships and patterns in the data, with a focus on predictive performance of various models that can be built to represent the underlying function generating the data. Topics to be covered: Linear Regression, Classification, Resampling Methods, Linear Model Selection and Regularization, Tree-Based Methods, Support Vector Machines, Unsupervised Learning (Clustering).  

Students with prior credit in INF 385T, topic: Intro to Machine Learning/SAL may not enroll in this class.