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Oct 09, 2026
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AI 430 Advanced Machine Learning
3 credits
This course provides an in-depth exploration of advanced machine learning techniques, building on foundational concepts and practical skills in machine learning. This course emphasizes theoretical underpinnings in machine learning models and algorithms, practical implementation, and real-world applications of cutting-edge methods. Primary topics include supervised and unsupervised learning, deep learning, probabilistic graphical models, and optimization techniques in learning problems. Hands-on projects will involve implementing models from scratch as well as using modern frameworks and analyzing performance and behaviors from both theoretical and empirical perspectives. The course also covers emerging trends in machine learning, such as interpretability, fairness, and robustness, preparing students for research or industry roles in this rapidly evolving field. By the end, students will be equipped to design, evaluate, and deploy sophisticated machine learning systems.
Prerequisite(s): CPS 232, AI 330, and MTH 250
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