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braga neto u fundamentals pattern recognition machine learning 2ed 2024

Type:: E-books
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Uploaded On:: Aug. 8, 2024, 7:16 a.m.
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Textbook in PDF format This book is a concise but thorough introduction to 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 deep neural networks and Gaussian process regression. The Second Edition is thoroughly revised, featuring a new chapter on the emerging topic of physics-informed Machine Learning and additional material on deep neural networks. Combining theory and practice, this book is suitable for the graduate or advanced undergraduate level classroom and self-study. It fills the need of a mathematically-rigorous text that is relevant to the practitioner as well, with datasets from applications in bioinformatics and materials informatics 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 Keras/Tensorflow. All plots in the text were generated using Python scripts and Jupyter notebooks, which can be downloaded from the book website. Introduction Optimal Classifcation Sample-Based Classifcation Parametric Classifcation Nonparametric Classifcation Function-Approximation Classifcation Error Estimation for Classifcation Model Selection for Classifcation Dimensionality Reduction Clustering Regression Physics-Informed Machine Learning Appendix A1 Probability Theory Asymptotic Theorems A2 Basic Matrix Theory A3 Basic Lagrange-Multiplier Optimization A4 Proof of the Cover-Hart Theorem A5 Proof of Stone’s Theorem A6 Proof of the Vapnik-Chervonenkis Theorem A7 Proof of Convergence of the EM Algorithm A8 Data Sets Used in the Book

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Braga-Neto U. Fundamentals of Pattern Recognition and Machine Learning 2ed 2024.pdf 14.7 MB

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braga neto u fundamentals pattern recognition machine learning 2ed 2024

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