Mathematical Theories of Machine Learning - Theory and Applications PDF Download

Author: Bin Shi
Publisher: Springer
ISBN: 3030170764
Category : Technology & Engineering
Languages : en
Pages : 138

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Book Description
This book studies mathematical theories of machine learning. The first part of the book explores the optimality and adaptivity of choosing step sizes of gradient descent for escaping strict saddle points in non-convex optimization problems. In the second part, the authors propose algorithms to find local minima in nonconvex optimization and to obtain global minima in some degree from the Newton Second Law without friction. In the third part, the authors study the problem of subspace clustering with noisy and missing data, which is a problem well-motivated by practical applications data subject to stochastic Gaussian noise and/or incomplete data with uniformly missing entries. In the last part, the authors introduce an novel VAR model with Elastic-Net regularization and its equivalent Bayesian model allowing for both a stable sparsity and a group selection.

Author: Bin Shi
Publisher:
ISBN: 9783030170776
Category : Big data
Languages : en
Pages : 133

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Book Description
This book studies mathematical theories of machine learning. The first part of the book explores the optimality and adaptivity of choosing step sizes of gradient descent for escaping strict saddle points in non-convex optimization problems. In the second part, the authors propose algorithms to find local minima in nonconvex optimization and to obtain global minima in some degree from the Newton Second Law without friction. In the third part, the authors study the problem of subspace clustering with noisy and missing data, which is a problem well-motivated by practical applications data subject to stochastic Gaussian noise and/or incomplete data with uniformly missing entries. In the last part, the authors introduce an novel VAR model with Elastic-Net regularization and its equivalent Bayesian model allowing for both a stable sparsity and a group selection. Provides a thorough look into the variety of mathematical theories of machine learning Presented in four parts, allowing for readers to easily navigate the complex theories Includes extensive empirical studies on both the synthetic and real application time series data.

Author: Sanjay Misra
Publisher: Springer Nature
ISBN: 3030691438
Category : Computers
Languages : en
Pages : 746

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Book Description
This book constitutes revised selected papers from the Third International Conference on Information and Communication Technology and Applications, ICTA 2020, held in Minna, Nigeria, in November 2020. Due to the COVID-19 pandemic the conference was held online. The 67 full papers were carefully reviewed and selected from 234 submissions. The papers are organized in the topical sections on Artificial Intelligence, Big Data and Machine Learning; Information Security Privacy and Trust; Information Science and Technology.

Author: Utku Kose
Publisher: Academic Press
ISBN: 0323907709
Category : Science
Languages : en
Pages : 814

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Book Description
Data Science for COVID-19, Volume 2: Societal and Medical Perspectives presents the most current and leading-edge research into the applications of a variety of data science techniques for the detection, mitigation, treatment and elimination of the COVID-19 virus. At this point, Cognitive Data Science is the most powerful tool for researchers to fight COVID-19. Thanks to instant data-analysis and predictive techniques, including Artificial Intelligence, Machine Learning, Deep Learning, Data Mining, and computational modeling for processing large amounts of data, recognizing patterns, modeling new techniques, and improving both research and treatment outcomes is now possible. - Provides a leading-edge survey of Data Science techniques and methods for research, mitigation and the treatment of the COVID-19 virus - Integrates various Data Science techniques to provide a resource for COVID-19 researchers and clinicians around the world, including the wide variety of impacts the virus is having on societies and medical practice - Presents insights into innovative, data-oriented modeling and predictive techniques from COVID-19 researchers around the world, including geoprocessing and tracking, lab data analysis, and theoretical views on a variety of technical applications - Includes real-world feedback and user experiences from physicians and medical staff from around the world for medical treatment perspectives, public safety policies and impacts, sociological and psychological perspectives, the effects of COVID-19 in agriculture, economies, and education, and insights on future pandemics

Author: Xavier Vasques
Publisher: John Wiley & Sons
ISBN: 1394220626
Category : Computers
Languages : en
Pages : 516

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Book Description
Machine Learning Theory and Applications Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries Machine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics, including data preparation, feature engineering techniques, commonly utilized machine learning algorithms like support vector machines and neural networks, as well as generative AI and foundation models. To facilitate the creation of machine learning pipelines, a dedicated open-source framework named hephAIstos has been developed exclusively for this book. Moreover, the text explores the fascinating domain of quantum machine learning and offers insights on executing machine learning applications across diverse hardware technologies such as CPUs, GPUs, and QPUs. Finally, the book explains how to deploy trained models through containerized applications using Kubernetes and OpenShift, as well as their integration through machine learning operations (MLOps). Additional topics covered in Machine Learning Theory and Applications include: Current use cases of AI, including making predictions, recognizing images and speech, performing medical diagnoses, creating intelligent supply chains, natural language processing, and much more Classical and quantum machine learning algorithms such as quantum-enhanced Support Vector Machines (QSVMs), QSVM multiclass classification, quantum neural networks, and quantum generative adversarial networks (qGANs) Different ways to manipulate data, such as handling missing data, analyzing categorical data, or processing time-related data Feature rescaling, extraction, and selection, and how to put your trained models to life and production through containerized applications Machine Learning Theory and Applications is an essential resource for data scientists, engineers, and IT specialists and architects, as well as students in computer science, mathematics, and bioinformatics. The reader is expected to understand basic Python programming and libraries such as NumPy or Pandas and basic mathematical concepts, especially linear algebra.

Author: Bernd-Arno Behrens
Publisher: Springer Nature
ISBN: 303078424X
Category : Technology & Engineering
Languages : en
Pages : 693

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Book Description
This congress proceedings provides recent research on leading-edge manufacturing processes. The aim of this scientific congress is to work out diverse individual solutions of "production at the leading edge of technology" and transferable methodological approaches. In addition, guest speakers with different backgrounds will give the congress participants food for thoughts, interpretations, views and suggestions. The manufacturing industry is currently undergoing a profound structural change, which on the one hand produces innovative solutions through the use of high-performance communication and information technology, and on the other hand is driven by new requirements for goods, especially in the mobility and energy sector. With the social discourse on how we should live and act primarily according to guidelines of sustainability, structural change is gaining increasing dynamic. It is essential to translate politically specified sustainability goals into socially accepted and marketable technical solutions. Production research is meeting this challenge and will make important contributions and provide innovative solutions from different perspectives.

Author: Jayakumar Singaram
Publisher: Springer Nature
ISBN: 3031392442
Category : Technology & Engineering
Languages : en
Pages : 173

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Book Description
This textbook presents multiple facets of design, development and deployment of deep learning networks for both students and industry practitioners. It introduces a deep learning tool set with deep learning concepts interwoven to enhance understanding. It also presents the design and technical aspects of programming along with a practical way to understand the relationships between programming and technology for a variety of applications. It offers a tutorial for the reader to learn wide-ranging conceptual modeling and programming tools that animate deep learning applications. The book is especially directed to students taking senior level undergraduate courses and to industry practitioners interested in learning about and applying deep learning methods to practical real-world problems.

Author: Tilottama Goswami
Publisher: Academic Press
ISBN: 0323972527
Category : Computers
Languages : en
Pages : 398

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Book Description
Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and roles of statistics, exploratory data analysis and machine learning. The various aspects of Machine Learning are discussed along with basics of statistics. Concepts are presented with simple examples and graphical representation for better understanding of techniques. This book takes a holistic approach – putting key concepts together with an in-depth treatise on multi-disciplinary applications of machine learning. New case studies and research problem statements are discussed, which will help researchers in their application areas based on the concepts of statistics and machine learning. Statistical Modeling in Machine Learning: Concepts and Applications will help statisticians, machine learning practitioners and programmers solving various tasks such as classification, regression, clustering, forecasting, recommending and more. - Provides a comprehensive overview of the state-of-the-art in statistical concepts applied to Machine Learning with the help of real-life problems, applications and tutorials - Presents a step-by-step approach from fundamentals to advanced techniques - Includes Case Studies with both successful and unsuccessful applications of Machine Learning to understand challenges in its implementation, along with worked examples

Author: Mathukumalli Vidyasagar
Publisher: Springer Science & Business Media
ISBN: 1447137485
Category : Technology & Engineering
Languages : en
Pages : 498

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Book Description
How does a machine learn a new concept on the basis of examples? This second edition takes account of important new developments in the field. It also deals extensively with the theory of learning control systems, now comparably mature to learning of neural networks.

Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1799804151
Category : Computers
Languages : en
Pages : 1707

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Book Description
Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis. Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of deep learning.