An Introduction to Machine Learning

This book PDF is perfect for those who love Computers genre, written by Miroslav Kubat and published by Springer Nature which was released on 25 September 2021 with total hardcover pages 458. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related An Introduction to Machine Learning books below.

An Introduction to Machine Learning
An Introduction to Machine Learning
Author : Miroslav Kubat
File Size : 44,8 Mb
Publisher : Springer Nature
Language : English
Release Date : 25 September 2021
ISBN : 9783030819354
Pages : 458 pages
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An Introduction to Machine Learning by Miroslav Kubat Book PDF Summary

This textbook offers a comprehensive introduction to Machine Learning techniques and algorithms. This Third Edition covers newer approaches that have become highly topical, including deep learning, and auto-encoding, introductory information about temporal learning and hidden Markov models, and a much more detailed treatment of reinforcement learning. The book is written in an easy-to-understand manner with many examples and pictures, and with a lot of practical advice and discussions of simple applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, rule-induction programs, artificial neural networks, support vector machines, boosting algorithms, unsupervised learning (including Kohonen networks and auto-encoding), deep learning, reinforcement learning, temporal learning (including long short-term memory), hidden Markov models, and the genetic algorithm. Special attention is devoted to performance evaluation, statistical assessment, and to many practical issues ranging from feature selection and feature construction to bias, context, multi-label domains, and the problem of imbalanced classes.

An Introduction to Machine Learning

An Introduction to Machine Learning by Miroslav Kubat

This textbook offers a comprehensive introduction to Machine Learning techniques and algorithms. This Third Edition covers newer approaches that have become highly topical, including deep learning, and auto-encoding, introductory information about temporal learning and hidden Markov models, and a much more detailed treatment of reinforcement learning. The book is written

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An Introduction to Machine Learning

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Introduction to Machine Learning

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Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation

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A Concise Introduction to Machine Learning

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