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**University of California, Berkeley**, Fall 2023
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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.
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Here are the Gradescope/Ed codes (you should self-enroll in these). We won't post any materials on bCourses.
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- Gradescope: E73744
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- Ed: [https://edstem.org/us/join/fCBF32](https://edstem.org/us/join/fCBF32)
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Professors will post slides prior to lecture at this Google Drive [folder](https://drive.google.com/drive/u/0/folders/1eZplfXbQytcvNTK2ssLYinYSF4rJ1eWI) (for faster access). The material here is redundant with the website, but it may take up to a day or two for the website to get updated with the slides after lecture. The "Post-Lecture" subfolder contains updates to slides that the Professors may make right after lecture.
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