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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.
Zhoujie Ding
NLP, LLMs
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Neil Band
LLMs, uncertainty quantification
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Ed Chen
multi-objective optimization, preference learning, healthcare
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Ryan A. Chi
NLP, LLM reasoning, dialogue
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Ryan Li
NLP, Large Language Models, Agentic AI
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Chris Fifty
multimodal / meta / multi-task / transfer learning
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Charlie Marx
uncertainty quantification, probabilistic modeling, time series, diffusion
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Roshni Sahoo
causal inference
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Boxin Zhang
physical sciences, statistical learning
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Paris Zhang
CV, VLMs, visual generative models
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Kamyar Salahi
NLP, VLMs, Generative Models, LLMs
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Sergio Charles
Geometric Deep Learning, GNNs, Generative Models
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Roger Dai
Embodied AI, Robot Learning, CV
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Medhanie Irgau
Transfer Learning, Generative Models, Uncertainty Quantification
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Tejas Narayanan
CV, RL
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