INF 397 - Research in Information Studies: Statistical Analysis & Learning
Graduate standing. Additional prerequisites may vary with the topic.
Methods and subjects of research in information studies. May be repeated for credit when the topics vary.
Three lecture hours a week for one semester.
May be repeated for credit when the topics vary.
Effective Fall 2014, MSIS students must earn a grade of B or better in the MSIS core courses (below) in order for the courses to apply to the master's degree. A grade of B- does NOT satisfy this requirement.
Instructor: Varun Rai
Cross-listing of PA 397C, offered by the LBJ School of Public Affairs.
Statistical Analysis & Learning
Topic Description: 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. The course starts with a review of basic statistical concepts and linear regression. But the course will focus mostly on classification and clustering based on non-regression techniques such as tree-based approaches, support vector machines, and unsupervised learning. In the problem sets and tutorials we will examine applications in: healthcare; energy; transportation; online markets; and patent systems.
Topics will include Linear Regression, Classification, Resampling Methods, Linear Model Selection and Regularization, Tree-Based Methods, Support Vector Machines, Unsupervised Learning. In covering the material from the assigned textbook (see below), this course will emphasize both on formulaic and conceptual understanding of the discussed methods. As necessary, the instructor will draw on material from outside the textbook for driving conceptual clarity.