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By: Hemachandran K (Edited by) , Parveen Singla (Edited by) , Preetha Mary George (Edited by) , Shubham Tayal (Edited by) , Utku Kose (Edited by)
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This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning, which is followed by Gaussian process introduction, classification, regression, covariance, and performance analysis of Gaussian processes with other models.
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This book is aimed at graduates, researchers, and professionals in the field of data science and machine learning.
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