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Books > Computing & IT > Applications of computing > Image processing
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Fundamentals of Pattern Recognition and Machine Learning (Paperback, 1st ed. 2020)
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Fundamentals of Pattern Recognition and Machine Learning (Paperback, 1st ed. 2020)
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Fundamentals of Pattern Recognition and Machine Learning is
designed for a one or two-semester introductory course in Pattern
Recognition or Machine Learning at the graduate or advanced
undergraduate level. The book combines theory and practice and is
suitable to the classroom and self-study. It has grown out of
lecture notes and assignments that the author has developed while
teaching classes on this topic for the past 13 years at Texas
A&M University. The book is intended to be concise but
thorough. It does not attempt an encyclopedic approach, but covers
in significant detail the tools commonly used in pattern
recognition and machine learning, including classification,
dimensionality reduction, regression, and clustering, as well as
recent popular topics such as Gaussian process regression and
convolutional neural networks. In addition, the selection of topics
has a few features that are unique among comparable texts: it
contains an extensive chapter on classifier error estimation, as
well as sections on Bayesian classification, Bayesian error
estimation, separate sampling, and rank-based classification. The
book is mathematically rigorous and covers the classical theorems
in the area. Nevertheless, an effort is made in the book to strike
a balance between theory and practice. In particular, examples with
datasets from applications in bioinformatics and materials
informatics are used throughout to illustrate the theory. These
datasets are available from the book website to be used in
end-of-chapter coding assignments based on python and scikit-learn.
All plots in the text were generated using python scripts, which
are also available on the book website.
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