Course Description This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
To help with project advice, each member of course staff's ML expertise is also listed below.
Aman Patel
Computational Biology, General ML
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Shiny Weng
Statistical Learning, Trustworthiness, Computer Vision
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Zipeng Fu
Robot Learning, Reinforcement Learning
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Jacob Frausto
Computer Vision, Reinforcement Learning, LLM agents
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Minsik Oh
Natural Language Processing, LLM Post-training, Representation Learning
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Thomas Chen
ML theory
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Amy Guan
Statistical Learning
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Priya Khandelwal
Computer Vision, GNNs, ML Systems, LLMs
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John So
Robot Learning, RL, CV
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Arya B.
NLPs, GNNs, Systems, Robotics, CV
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