Graduate and Doctoral Courses

Last Offered Spring 2024
Schedule & Location: TR 11:35-12:50, 152 DSL

ISC 4933/ISC 5935. Computational Probabilistic Modeling (3). Prerequisites: MAC 2312 - Calculus II, MAS 3105 – Applied Linear Algebra, and STA 4442/5440: Introduction To Probability or STA 4321/5323: Introduction to Mathematical Statistics, or the permission of the instructor. In this course, students are introduced to probabilistic programming and modeling for modern data science and machine learning applications. Algorithms for predictive inference are covered from a theoretical and practical viewpoint with an emphasis on implementation in Python. Topics include an introduction to probability and learning theory, graph-based methods, machine learning with neural networks, dimensionality reduction, and algorithms for big data. [source]