IS 5321 Principles of Optimization and Inference 3.0 Credits
Develops computational and mathematical techniques needed to develop and implement analytic methods, including model training, statistical inference, approximation, and estimation. Building on foundational calculus and computational skills, this class focuses on more complex analytic techniques, alongside practice-oriented studies of quantitative foundations. Breaks down components of advanced toolkits in numerical computing and implements techniques from statistical inference and optimization used in learning applications. Applies foundational knowledge on the definition and function of analytic processes to support prediction, measurement, and optimization. Project-based work implements relevant techniques and interprets effects related to numerical representation and parameterization.
Repeat Status: Not repeatable for credit
Prerequisites: IS 5320 [Min Grade: D] or CS 5030 [Min Grade: D] or DSCI 501 [Min Grade: D] or CS 502 [Min Grade: D]