Computational Optimization for Ai

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1500

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MATH 102 and COMP 182 and MATH 355 Description: This course provides the mathematical and computational foundations necessary for understanding mod-ern AI/ML systems, with a focus on optimization and computational perspectives. The course interweaves three main threads: (1) computational linear algebra through the lens of optimization problems, (2) multi-variate calculus and optimization concepts essential for understanding learning algorithms, and (3) practical implementation using Python’s scientific computing ecosystem. Students will learn to implement and analyze fundamental algorithms for machine learning, developing both theoretical understanding and practical coding skills. The course emphasizes computational efficiency, algorithm implementation, and the connections between mathematical theory and practical AI applications. This class is intended to prepare students for upper-level AI classes→COMP 282→unlocks 2 courses

2 mapped courses list COMP 282 as a prerequisite. See on the map →

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