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Machine Learning

Machine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by;

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Learning Bayesian Models with R: Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems

Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems About This Book;

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Variational Bayesian Learning Theory

Variational Bayesian learning is one of the most popular methods in machine learning. Designed for researchers and graduate students;

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Computational Learning And Probabilistic Reasoning

learning. The contributions in this volume describe and explore the current developments in computer science and theoretical statistics which;

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Bayesian Reasoning and Machine Learning

"""Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range;

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Advances in Artificial Intelligence

papers are organized in topical sections on Bayesian networks and uncertainty modeling; fuzzy logic and soft computing; knowledge representation;

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Machine Learning and Visual Perception

Machine Learning and Visual Perception provides an up-to-date overview on the topic, including the PAC model, decision tree, Bayesian;

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Bayesian Optimization with Application to Computer Experiments

This book introduces readers to Bayesian optimization, highlighting advances in the field and showcasing its successful applications to;

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Bayesian Speech & Language Processing

With this comprehensive guide you will learn how to apply Bayesian machine learning techniques systematically to solve various problems;

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Principles Of Quantum Artificial Intelligence

topics include quantum machine learning, quantum-like Bayesian networks and mind in Everett many-worlds.;

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Machine Learning Theoretical Foundations and Practical Applications

in conjunction with 16th edition of ICDCIT. The book covers topics, like machine learning and its applications, statistical learning, neural network;

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Probabilistic Machine Learning

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian;

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Introduction to Machine Learning

, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing;

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Thoughtful Machine Learning

-learning algorithms, including Naive Bayesian classifiers and Neural Networks. Machine-learning algorithms often have tests baked in, but they;

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Abductive Reasoning and Learning

to all areas of applied reasoning, including artificial intelligence, philosophy of science, machine learning, data mining and decision;

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Abductive Reasoning and Learning

to all areas of applied reasoning, including artificial intelligence, philosophy of science, machine learning, data mining and decision;

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Case-Based Reasoning

Case-based reasoning is a methodology with a long tradition in artificial intelligence that brings together reasoning and machine learning;

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Bayesian Analysis in Natural Language Processing

Bayesian learning papers in NLP and to do research in the area. These methods and algorithms are partially borrowed from both machine learning and;

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Machine Learning and Artificial Intelligence in Geosciences

Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly;

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Machine Learning

classic and commonly used machine learning methods, such as decision trees, neural networks, support vector machines, Bayesian classifiers;

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Machine Learning Algorithms

Explore statistics and complex mathematics for data-intensive applications Discover new developments in EM algorithm, PCA, and bayesian regression;

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Pattern Recognition and Machine Learning

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms;

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Emerging Paradigms in Machine Learning

This book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms;

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Advanced Methodologies for Bayesian Networks

reviewed and selected from numerous submissions. In the International Workshop on Advanced Methodologies for Bayesian Networks (AMBN), the;

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Machine Learning for Finance

products. It discusses how to fight bias in machine learning and ends with an exploration of Bayesian inference and probabilistic programming. What;

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Lazy Learning

volume in AI on this topic, whose many synonyms include instance-based', memory-based'. exemplar-based', and local learning', and whose topic;

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Lazy Learning

edited volume in AI on this topic, whose many synonyms include `instance-based', `memory-based'. `exemplar-based', and `local learning', and;

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