Overview
- Offers a comprehensive introduction to the foundations of machine learning in a very easy-to-understand manner
- In addition to describing techniques and algorithms, each tool is applied to their appropriate situations
- Teaching resources include a Solutions Manual to end-of-chapter exercises, with presentation slides
- Request lecturer material: sn.pub/lecturer-material
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Table of contents (21 chapters)
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Front Matter
Keywords
- Bayesian classifiers
- boosting
- computational learning theory
- decision trees
- genetic algorithms
- linear and polynomial classifiers
- nearest neighbor classifier
- neural networks
- performance evaluation
- reinforcement learning
- statistical learning
- time-varying classes, imbalanced representation
- artificial intelligence
- machine learning
- data mining
- deep learning
- unsupervised learning
About this book
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.
Authors and Affiliations
About the author
Miroslav Kubat, Associate Professor at the University of Miami, has been teaching and studying machine learning for over 25 years. He has published more than 100 peer-reviewed papers, co-edited two books, served on the program committees of over 60 conferences and workshops, and is an editorial board member of three scientific journals. He is widely credited with co-pioneering research in two major branches of the discipline: induction of time-varying concepts and learning from imbalanced training sets. He also contributed to research in induction from multi-label examples, induction of hierarchically organized classes, genetic algorithms, and initialization of neural networks. Professor Kubat is also known for his many practical applications of machine learning, ranging from oil-spill detection in radar images to text categorization to tumor segmentation in MR images.
Bibliographic Information
Book Title: An Introduction to Machine Learning
Authors: Miroslav Kubat
DOI: https://doi.org/10.1007/978-3-030-81935-4
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-81934-7Published: 27 September 2021
eBook ISBN: 978-3-030-81935-4Published: 25 September 2021
Edition Number: 3
Number of Pages: XVIII, 458
Number of Illustrations: 109 b/w illustrations, 5 illustrations in colour
Topics: Artificial Intelligence, Big Data/Analytics, Probability and Statistics in Computer Science, Data Mining and Knowledge Discovery, Algorithm Analysis and Problem Complexity, Computational Intelligence