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COMP SCI 761

MATHEMATICAL FOUNDATIONS OF MACHINE LEARNING

3 credits.

Mathematical foundations of machine learning theory and algorithms. Probabilistic, algebraic, and geometric models and representations of data, mathematical analysis of state-of-the-art learning algorithms and optimization methods, and applications of machine learning. Knowledge of probability [such as MATH/STAT 431 or COMP SCI/E C E 561 ] and linear algebra [such as MATH 341 or M E/COMP SCI/E C E 532 ] is required.

Prerequisites & course information

Requisites: Graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Derive and apply mathematical tools for machine learning from probability, statistics, linear algebra, and optimization Audience: Graduate 2. Perform mathematical analysis and characterization of generative and discriminative models Audience: Graduate 3. Perform mathematical analysis of machine learning algorithms Audience: Graduate 4. Perform derivation of basic machine learning error bounds and related performance analysis Audience: Graduate 5. Read and understand theoretical papers from machine learning conferences Audience: Graduate Learning Outcomes: 1. Derive and apply mathematical tools for machine learning from probability, statistics, linear algebra, and optimization Audience: Graduate 2. Perform mathematical analysis and characterization of generative and discriminative models Audience: Graduate 3. Perform mathematical analysis of machine learning algorithms Audience: Graduate 4. Perform derivation of basic machine learning error bounds and related performance analysis Audience: Graduate 5. Read and understand theoretical papers from machine learning conferences Audience: Graduate

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