Pattern Recognition and Machine Learning
by Christopher M. Bishop · Christopher M. Bishop
Graduate-level text presenting machine learning from a Bayesian probabilistic viewpoint: linear models, kernel methods, graphical models, mixture models, EM, variational inference and sampling. Requires comfort with linear algebra and calculus, and rewards careful work through its exercises.
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