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Pattern Recognition and Machine Learning provides the student a detailed overview of engineering concepts, including probability theory, probability distributions, linear models for regression, and linear models for classification. This textbook is direct and concise with systematic content designed to engage today’s student through visually engaging content. There is a wide variety of assignment types that help students master engineering concepts & build real-world working capability. Pattern Recognition and Machine Learning goes into neural networks, kernel methods, sparse kernel machines, graphical models, and mixture models and EM. A chapter is broken down into continuous latent variables. Buy the textbook Pattern Recognition and Machine Learning and be confident in your engineering education and career.
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