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CS 189/289A (Introduction to Machine Learning) covers: Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression a
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Home | CS 189/289A Skip to main content Menu Expand (external link) Document Search Copy Copied CS 189/289A Home Calendar Syllabus Course Staff Resources Past Exams This site uses Just the Docs , a documentation theme for Jekyll. Introduction to Machine Learning University of California, Berkeley , Fall 2023 Welcome to CS 189/289A! This class covers theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; deep learning models including CNNs, Transformers, graph neural networks for vision and language tasks; and Markovian models for reinforcement learning and robotics. Here are the Gradescope/Ed codes (you should self-enroll in these). We won’t post any materials on bCourses. Gradescope: E73744 Ed: ht...
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