Machine learning a Bayesian and optimization perspective

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文件名称：Machine learning a Bayesian and optimization perspective
文件大小：26.56MB
文件格式：ZIP
更新时间：2022-01-05 14:12:13
機器學習 貝葉斯方法 This book is about learning from data; in particular, our intent is to detect and unveil a possible hidden structure and regularity patterns associated with their generation mechanism. This information in turn helps our analysis and understanding of the nature of the data, which can be used to make predictions for the future. Besides modeling the underlying structure, a major direction of signifcant interest in Machine Learning is to develop effcient algorithms for designing the models and also for analysis and prediction. The latter part is gaining importance in the dawn of what we call the big data era, when one has to deal with massive amounts of data, which may be represented in spaces of very large dimensionality. Analyzing data for such applications sets demands on algorithms to be computationally effcient and at the same time robust in their performance, because some of these data are contaminated with large noise and also, in some cases, the data may have missing values.

【文件预览】：
Chapter-8---Parameter-Learning--A-Convex-Analytic-Path_2015_Machine-Learning.pdf
Chapter-7---Classification--A-Tour-of-the-Classics_2015_Machine-Learning.pdf
Chapter-9---Sparsity-Aware-Learning--Concepts-and-Theoreti_2015_Machine-Lear.pdf
Dedication_2015_Machine-Learning.pdf
Chapter-11---Learning-in-Reproducing-Kernel-Hilbert-Spa_2015_Machine-Learnin.pdf
Appendix-B---Probability-Theory-and-Statistics_2015_Machine-Learning.pdf
Chapter-15---Probabilistic-Graphical-Models--Part-I_2015_Machine-Learning.pdf
Copyright_2015_Machine-Learning.pdf
Chapter-13---Bayesian-Learning--Approximate-Inference-and-N_2015_Machine-Lea.pdf
Chapter-2---Probability-and-Stochastic-Processes_2015_Machine-Learning.pdf
Chapter-12---Bayesian-Learning--Inference-and-the-EM-Alg_2015_Machine-Learni.pdf
Front-Matter_2015_Machine-Learning.pdf
Chapter-4---Mean-Square-Error-Linear-Estimation_2015_Machine-Learning.pdf
Appendix-A---Linear-Algebra_2015_Machine-Learning.pdf
Notation_2015_Machine-Learning.pdf
Chapter-5---Stochastic-Gradient-Descent--The-LMS-Algorithm_2015_Machine-Lear.pdf
Chapter-6---The-Least-Squares-Family_2015_Machine-Learning.pdf
Chapter-14---Monte-Carlo-Methods_2015_Machine-Learning.pdf
Chapter-17---Particle-Filtering_2015_Machine-Learning.pdf
Chapter-3---Learning-in-Parametric-Modeling--Basic-Concept_2015_Machine-Lear.pdf
Chapter-10---Sparsity-Aware-Learning--Algorithms-and-Appl_2015_Machine-Learn.pdf
Chapter-19---Dimensionality-Reduction-and-Latent-Variable_2015_Machine-Learn.pdf
Appendix-C---Hints-on-Constrained-Optimization_2015_Machine-Learning.pdf
Chapter-1---Introduction_2015_Machine-Learning.pdf
Acknowledgments_2015_Machine-Learning.pdf
Chapter-16---Probabilistic-Graphical-Models--Part-II_2015_Machine-Learning.pdf
Preface_2015_Machine-Learning.pdf
Index_2015_Machine-Learning.pdf
Chapter-18---Neural-Networks-and-Deep-Learning_2015_Machine-Learning.pdf