Fundamentals of Pattern Recognition and Machine Learning
Publication year: 2020
ISBN: 978-3-030-27656-0
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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.
Subject: Computer Science, Pattern Recognition, Machine Learning, Classification, Regression, Clustering, Feature Selection, Linear Discriminant Analysis, Vapnik-Chervonenkis Theory, Perceptron, Neural Networks, Support Vector Machines, Multidimensional Scaling, Decision Trees, Principal Component Analysis, Gaussian Process, Cross-Validation, Bootstrap, K-means Clustering, Gaussian Mixture Modeling
