Fall 2023

INF 397 Research in Information Studies : Introduction to Machine Learning/SAL

Unique ID: 28874


02:00 PM - 05:00 PM  SRH 3.122

Cross-listing of P A 397C, hosted by the LBJ School of Public Affairs.

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In Person


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.


Graduate standing. Additional prerequisites may vary with the topic.


Restricted to graduate students in the School of Information through registration periods 1 and 2. Outside students will be permitted to join our waitlists beginning with registration period 3.