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BOOK SERIES


Chapman & Hall/CRC Machine Learning & Pattern Recognition


About the Series

The field of machine learning has experienced significant growth in the past two decades as new algorithms and techniques have been developed and new research and applications have emerged. This series reflects the latest advances and applications in machine learning and pattern recognition through the publication of a broad range of reference works, textbooks, and handbooks. We are looking for single authored works and edited collections that will:

  • Present the latest research and applications in the field, including new mathematical, statistical, and computational methods and techniques
  • Provide both introductory and advanced material for students and professionals
  • Cover a broad range of topics around learning and inference

The inclusion of concrete examples, applications, and methods is highly encouraged. The scope of the series includes, but is not limited to, titles in the areas of machine learning, pattern recognition, computational intelligence, robotics, computational/statistical learning theory, natural language processing, computer vision, game AI, game theory, neural networks, and computational neuroscience. We are also willing to consider other relevant topics, such as machine learning applied to bioinformatics or cognitive science, which might be proposed by potential contributors.

For more information or to submit a book proposal for the series, please contact Randi Cohen, Publisher, CS and IT ([email protected]) or Elliott Morsia, Editor, CS ([email protected]).

20 Series Titles

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Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security

2nd Edition

Forthcoming

By Mark Stamp
October 04, 2022

Introduction to Machine Learning with Applications in Information Security, Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic applications. The book is accessible and doesn’t prove ...

Entropy Randomization in Machine Learning

Entropy Randomization in Machine Learning

1st Edition

By Yuri S. Popkov, Alexey Yu. Popkov, Yuri A. Dubnov
August 09, 2022

Entropy Randomization in Machine Learning presents a new approach to machine learning—entropy randomization—to obtain optimal solutions under uncertainty (uncertain data and models of the objects under study). Randomized machine-learning procedures involve models with random parameters and maximum ...

Transformers for Machine Learning A Deep Dive

Transformers for Machine Learning: A Deep Dive

1st Edition

By Uday Kamath, Kenneth L. Graham, Wael Emara
May 25, 2022

Transformers are becoming a core part of many neural network architectures, employed in a wide range of applications such as NLP, Speech Recognition, Time Series, and Computer Vision. Transformers have gone through many adaptations and alterations, resulting in newer techniques and methods. ...

Artificial Intelligence and Causal Inference

Artificial Intelligence and Causal Inference

1st Edition

By Momiao Xiong
March 08, 2022

Artificial Intelligence and Causal Inference address the recent development of  relationships between  artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying ...

Deep Learning and Linguistic Representation

Deep Learning and Linguistic Representation

1st Edition

By Shalom Lappin
April 27, 2021

The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this ...

Utility-Based Learning from Data

Utility-Based Learning from Data

1st Edition

By Craig Friedman, Sven Sandow
November 25, 2019

Utility-Based Learning from Data provides a pedagogical, self-contained discussion of probability estimation methods via a coherent approach from the viewpoint of a decision maker who acts in an uncertain environment. This approach is motivated by the idea that probabilistic models are usually not ...

Data Science and Machine Learning Mathematical and Statistical Methods

Data Science and Machine Learning: Mathematical and Statistical Methods

1st Edition

By Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman
November 22, 2019

"This textbook is a well-rounded, rigorous, and informative work presenting the mathematics behind modern machine learning techniques. It hits all the right notes: the choice of topics is up-to-date and perfect for a course on data science for mathematics students at the advanced undergraduate or ...

A Concise Introduction to Machine Learning

A Concise Introduction to Machine Learning

1st Edition

By A.C. Faul
August 12, 2019

The emphasis of the book is on the question of Why – only if why an algorithm is successful is understood, can it be properly applied, and the results trusted. Algorithms are often taught side by side without showing the similarities and differences between them. This book addresses the ...

Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security

1st Edition

By Mark Stamp
September 07, 2017

Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn’t prove theorems, or otherwise dwell on mathematical theory. ...

A First Course in Machine Learning

A First Course in Machine Learning

2nd Edition

By Simon Rogers, Mark Girolami
August 15, 2016

"A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes ...

Statistical Reinforcement Learning Modern Machine Learning Approaches

Statistical Reinforcement Learning: Modern Machine Learning Approaches

1st Edition

By Masashi Sugiyama
March 16, 2015

Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for ...

Sparse Modeling Theory, Algorithms, and Applications

Sparse Modeling: Theory, Algorithms, and Applications

1st Edition

By Irina Rish, Genady Grabarnik
December 01, 2014

Sparse models are particularly useful in scientific applications, such as biomarker discovery in genetic or neuroimaging data, where the interpretability of a predictive model is essential. Sparsity can also dramatically improve the cost efficiency of signal processing. Sparse Modeling: Theory, ...

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