Bộ sách luyện thi ETS 2023 LC&RC
199,000₫
299,000₫
Sách Fundamentals of Pattern Recognition and Machine Learning (Sách keo gáy, bìa mềm)
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.
Thể loại:Computers - Computer Science
Năm:2020
Ngôn ngữ:english
Trang:357
