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Course Descriptions

Graduate Course Descriptions

Graduate courses are 5000 and above. Graduate students can take a limited number of 4xxx-level courses but only if the course gives graduate credit

Bulletin CS Description

 

Special Notes

This section is used to note upcoming changes before they appear in the Bulletin.

Cmp Sci 5151 Statistical Methods for Computer Science

is a new course on statistical foundations for CS data science, also useful in AI, to be started likely FS24 or SP25.
Prerequisites: Graduate standing in Computer Science/Cybersecurity (Computer Science option) or, for non-Computer Science graduate students, consent of the instructor.
This course covers statistical inference with emphasis on applications and computer simulation. Topics may include multivariate distributions, transformations and combinations of random variables, sampling distributions, maximum likelihood, bootstrap, order statistics, hypothesis testing, likelihood ratio tests, Monte Carlo methods, Bayesian inference, and sufficient statistics. Students may not receive credit for both CMP SCI 4151 and CMP SCI 5151.

Cmp Sci 5710 Modern Computing

is a new course for graduate students in CS, started FS23.
Prerequisites: Graduate standing.
This course covers technical concepts and tools in a modern computing environment such as: file systems, command-line operations and productivity tools, communication with servers, software installation and deployment, basic data representation and computer organization/memory layers, virtualization and cloud.

CMP SCI 6390 Interpretable Machine Learning

is a new research course starting SP24.

Prerequisites: CMP SCI 5390 or CMP SCI 5340. This research course discusses classical, modern, and advanced methods for machine and deep learning interpretability. It focuses on the application, analysis, and evaluation of model-agnostic methods to interpret shallow and deep neural network models and their predictions.

CMP SCI 5622 Big Data

is a new course starting FS24.

This course introduces big data fundamentals and covers topics including a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with tools such as Spark MLlib, Hive and HBase. Students will also learn about the analytical processes and data systems available to build and empower data products.