Books in Machine learning

Machine Learning

3rd Edition
December 6, 2024
Sergios Theodoridis
English
Paperback
9 7 8 0 4 4 3 2 9 2 3 8 5
eBook
9 7 8 0 4 4 3 2 9 2 3 9 2

Machine Learning: From the Classics to Deep Networks, Transformers and Diffusion Models, Third Edition starts with the basics, including least squares regression and maximum likelihood methods, Bayesian decision theory, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines. Bayesian learning is treated in detail with emphasis on the EM algorithm and its approximate variational versions with a focus on mixture modelling, regression and classification. Nonparametric Bayesian learning, including Gaussian, Chinese restaurant, and Indian buffet processes are also presented. Monte Carlo methods, particle filtering, probabilistic graphical models with emphasis on Bayesian networks and hidden Markov models are treated in detail. Dimensionality reduction and latent variables modelling are considered in depth. Neural networks and deep learning are thoroughly presented, starting from the perceptron rule and multilayer perceptrons and moving on to convolutional and recurrent neural networks, adversarial learning, capsule networks, deep belief networks, GANs, and VAEs. The book also covers the fundamentals on statistical parameter estimation and optimization algorithms.Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all methods and techniques are explained in depth, supported by examples and problems, providing an invaluable resource to the student and researcher for understanding and applying machine learning concepts.

Machine Learning Models and Architectures for Biomedical Signal Processing

1st Edition
November 5, 2024
Suman Lata Tripathi + 3 more
English
Paperback
9 7 8 0 4 4 3 2 2 1 5 8 3
eBook
9 7 8 0 4 4 3 2 2 1 5 7 6

Machine Learning Models and Architectures for Biomedical Signal Processing presents the fundamental concepts of machine learning techniques for bioinformatics in an interactive way. The book investigates how efficient machine and deep learning models can support high-speed processors with reconfigurable architectures like graphic processing units (GPUs), Field programmable gate arrays (FPGAs), or any hybrid system. This great resource will be of interest to researchers working to increase the efficiency of hardware and architecture design for biomedical signal processing and signal processing techniques.

Artificial Intelligence of Things (AIoT)

1st Edition
September 11, 2024
Fadi Al-Turjman + 2 more
English
Paperback
9 7 8 0 4 4 3 2 6 4 8 2 5
eBook
9 7 8 0 4 4 3 2 6 4 8 3 2

Artificial Intelligence of Things (AIoT): Current and Future Trends brings together researchers and developers from a wide range of domains to share ideas on how to implement technical advances, create application areas for intelligent systems, and how to develop new services and smart devices connected to the Internet. Section One covers AIoT in Everything, providing a wide range of applications for AIoT methods and technologies. Section Two gives readers comprehensive guidance on AIoT in Societal Research and Development, with practical case studies of how AIoT is impacting cultures around the world. Section Three covers the impact of AIoT in educational settings.The book also covers new capabilities such as pervasive sensing, multimedia sensing, machine learning, deep learning, and computing power. These new areas come with various requirements in terms of reliability, quality of service, and energy efficiency.

Decision-Making Models

1st Edition
July 24, 2024
Tofigh Allahviranloo + 2 more
English
Paperback
9 7 8 0 4 4 3 1 6 1 4 7 6
eBook
9 7 8 0 4 4 3 1 6 1 4 8 3

Decision Making Models: A Perspective of Fuzzy Logic and Machine Learning presents the latest developments in the field of uncertain mathematics and decision science. The book aims to deliver a systematic exposure to soft computing techniques in fuzzy mathematics as well as artificial intelligence in the context of real-life problems and is designed to address recent techniques to solving uncertain problems encountered specifically in decision sciences. Researchers, professors, software engineers, and graduate students working in the fields of applied mathematics, software engineering, and artificial intelligence will find this book useful to acquire a solid foundation in fuzzy logic and fuzzy systems.Other areas of note include optimization problems and artificial intelligence practices, as well as how to analyze IoT solutions with applications and develop decision-making mechanisms realized under uncertainty.

Distributed Optimization and Learning

1st Edition
July 18, 2024
Zhongguo Li + 1 more
English
Paperback
9 7 8 0 4 4 3 2 1 6 3 6 7
eBook
9 7 8 0 4 4 3 2 1 6 3 7 4

Distributed Optimization and Learning: A Control-Theoretic Perspective illustrates the underlying principles of distributed optimization and learning. The book presents a systematic and self-contained description of distributed optimization and learning algorithms from a control-theoretic perspective. It focuses on exploring control-theoretic approaches and how those approaches can be utilized to solve distributed optimization and learning problems over network-connected, multi-agent systems. As there are strong links between optimization and learning, this book provides a unified platform for understanding distributed optimization and learning algorithms for different purposes.

Performance Enhancement and Control of Photovoltaic Systems

1st Edition
April 30, 2024
Saad Motahhir + 1 more
English
Paperback
9 7 8 0 4 4 3 1 3 3 9 2 3
eBook
9 7 8 0 4 4 3 1 3 3 9 3 0

Performance Enhancement and Control of Photovoltaic Systems brings together the latest advances in photovoltaic control and integration, with various embedded technologies applied to standalone and grid connected systems in normal and abnormal operating conditions, with new approaches intended to overcome a number of critical limitations in using PV technology. The book begins by introducing modern photovoltaic (PV) systems, system integration, materials, and thermodynamic analysis for improved performance, before examining applications in industrial processes, artificial neural network technology, and economic analysis of PV systems.In-depth chapters then demonstrate the use of advanced control and optimization techniques, covering the use of new embedded technologies, through different applications such as MPPT controllers, solar trackers, cleaning systems, cooling systems, and monitoring systems. Applications of photovoltaic energy systems in distributed generation, microgrid, and smart grid systems will be considered.

Federated Learning

1st Edition
February 9, 2024
Lam M. Nguyen + 2 more
English
Paperback
9 7 8 0 4 4 3 1 9 0 3 7 7
eBook
9 7 8 0 4 4 3 1 9 0 3 8 4

Federated Learning: Theory and Practi ce provides a holisti c treatment to federated learning as a distributed learning system with various forms of decentralized data and features. Part I of the book begins with a broad overview of opti mizati on fundamentals and modeling challenges, covering various aspects of communicati on effi ciency, theoretical convergence, and security. Part II featuresemerging challenges stemming from many socially driven concerns of federated learning as a future public machine learning service. Part III concludes the book with a wide array of industrial applicati ons of federated learning, as well as ethical considerations, showcasing its immense potential for driving innovation while safeguarding sensitive data.Federated Learning: Theory and Practi ce provides a comprehensive and accessible introducti on to federated learning which is suitable for researchers and students in academia, and industrial practitioners who seek to leverage the latest advance in machine learning for their entrepreneurial endeavors.

Trolley Crash

1st Edition
January 26, 2024
Peggy Wu + 3 more
English
Paperback
9 7 8 0 4 4 3 1 5 9 9 1 6
eBook
9 7 8 0 4 4 3 1 5 9 9 2 3

The prolific deployment of Artificial Intelligence (AI) across different fields has introduced novel challenges for AI developers and researchers. AI is permeating decision making for the masses, and its applications range from self-driving automobiles to financial loan approvals. With AI making decisions that have ethical implications, responsibilities are now being pushed to AI designers who may be far-removed from how, where, and when these ethical decisions occur.Trolley Crash: Approaching Key Metrics for Ethical AI Practitioners, Researchers, and Policy Makers provides audiences with a catalogue of perspectives and methodologies from the latest research in ethical computing. This work integrates philosophical and computational approaches into a unified framework for ethical reasoning in the current AI landscape, specifically focusing on approaches for developing metrics. Written for AI researchers, ethicists, computer scientists, software engineers, operations researchers, and autonomous systems designers and developers, Trolley Crash will be a welcome reference for those who wish to better understand metrics for ethical reasoning in autonomous systems and related computational applications.

Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications

1st Edition
January 19, 2024
D. Jude Hemanth
English
Paperback
9 7 8 0 4 4 3 2 2 0 0 9 8
eBook
9 7 8 0 4 4 3 2 2 0 1 0 4

Computational Intelligence for Sentiment Analysis in Natural Language Processing Applications provides a solution to this problem through detailed technical coverage of AI-based Sentiment Analysis methods for various applications. The book's authors provide readers with an in-depth look at the challenges and associated solutions, including case studies and real-world scenarios from across the globe. Development of scientific and enterprise applications are covered that will aid computer scientists in building practical/real-world AI-based Sentiment Analysis systems. With the vast increase in Big Data, computational intelligence approaches have become a necessity for Natural Language Processing and Sentiment Analysis in a wide range of decision-making application areas.

Metaheuristics Algorithms for Medical Applications

1st Edition
November 24, 2023
Mohamed Abdel-Basset + 2 more
English
Paperback
9 7 8 0 4 4 3 1 3 3 1 4 5
eBook
9 7 8 0 4 4 3 1 3 3 1 5 2

Metaheuristics Algorithms for Medical Applications: Methods and Applications provides readers with the most complete reference for developing metaheuristics techniques with machine learning for solving biomedical problems. This book is organized to present a stepwise progression beginning with the basics of metaheuristics, leading into methods and practices, and concluding with advanced topics. The first section of this book presents the fundamental concepts of metaheuristics and machine learning and provides a comprehensive taxonomic view of metaheuristics methods according to a variety of criteria such as data type, scope, and method. The second section of this book explains how to apply metaheuristics techniques for solving large-scale biomedical problems, including analysis and validation under different strategies. The final portion of the book focuses on advanced topics in metaheuristics in four different applications. Readers will discover a variety of new methods, approaches, and techniques, as well as a wide range of applications demonstrating key concepts in metaheuristics for biomedical science. This book provides a leading-edge resource for researchers in a variety of scientific fields who are interested in metaheuristics, including mathematics, biomedical engineering, computer science, biological sciences, and clinicians in medical practice.

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