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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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Probabilistic Deep Learning

Probabilistic Deep Learning shows how probabilistic deep learning models gives readers the tools to identify and account for uncertainty;

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Hardware-Aware Probabilistic Machine Learning Models

This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models;

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Machine Learning for Speaker Recognition

learning techniques for speaker recognition and featuring a range of probabilistic models, learning algorithms, case studies, and new trends and;

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Advances in Machine Learning and Computational Intelligence

This book gathers selected high-quality papers presented at the International Conference on Machine Learning and Computational Intelligence;

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Foundations of Data Science

algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including;

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Machine Learning

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled;

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Conformal and Probabilistic Prediction with Applications

This book constitutes the refereed proceedings of the 5th International Symposium on Conformal and Probabilistic Prediction with;

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Foundations of Probabilistic Programming

What does a probabilistic program actually compute? How can one formally reason about such probabilistic programs? This valuable guide;

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Density Ratio Estimation in Machine Learning

Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications;

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Density Ratio Estimation in Machine Learning

Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications;

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Machine Learning

selection and sparse learning, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning, and;

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A Brief Introduction to Machine Learning for Engineers

introduction, read this book; for a detailed survey of methods based on probabilistic models, check this reference; to learn about statistical learning;

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Data Mining and Machine Learning

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze;

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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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Machine Learning and Medical Imaging

cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also;

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Introducing Machine Learning

Master machine learning concepts and develop real-world solutions Machine learning offers immense opportunities, and;

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Computational Learning And Probabilistic Reasoning

techniques for solving some important problems in machine intelligence and pattern recognition, namely probabilistic reasoning and computational;

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Intelligence Science and Big Data Engineering. Big Data and Machine Learning

submissions.The papers are organized in two parts: visual data engineering; and big data and machine learning. They cover a large range of topics;

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Learning Probabilistic Graphical Models in R

Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R About This Book;

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Machine Learning in Bioinformatics

An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are;

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Fundamentals of Machine Learning

learning techniques. The following four chapters discuss more fundamental concepts that includes their formulation in a probabilistic context. This;

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Computational Learning Theory and Natural Learning Systems: Selecting Good Models

nineteen contributions cover learning theory, empirical comparisons of learning algorithms, the use of prior knowledge, probabilistic concepts, and;

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Machine Learning and Knowledge Discovery in Databases

and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches;

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Machine Learning and Knowledge Discovery in Databases

and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches;

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Machine Learning and Knowledge Discovery in Databases

; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical;

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Handbook of Probabilistic Models

Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields;

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