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Tag: Computation

  • Introduction to Machine Learning, fourth edition [Adaptive Computation and Machi



    Introduction to Machine Learning, fourth edition [Adaptive Computation and Machi

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    ne Learning series]

    Machine learning is an exciting and rapidly evolving field that has the potential to revolutionize the way we interact with technology. In the fourth edition of the Adaptive Computation and Machine Learning series, we delve into the latest advancements in machine learning and provide a comprehensive introduction to the field.

    This edition covers a wide range of topics, including supervised and unsupervised learning, deep learning, reinforcement learning, and more. We also explore cutting-edge techniques such as neural networks, support vector machines, and decision trees.

    Whether you are a novice looking to learn the basics of machine learning or an experienced practitioner seeking to stay current with the latest developments, this book has something for everyone. With clear explanations, practical examples, and hands-on exercises, you will gain a solid foundation in machine learning and be well-equipped to tackle real-world problems.

    Don’t miss out on this essential resource for anyone interested in machine learning. Get your copy of Introduction to Machine Learning, fourth edition today and start your journey towards mastering this exciting field.
    #Introduction #Machine #Learning #fourth #edition #Adaptive #Computation #Machi,machine learning: an applied mathematics introduction

  • Introduction to Machine Learning, fourth edition [Adaptive Computation and Machi



    Introduction to Machine Learning, fourth edition [Adaptive Computation and Machi

    Price : 48.35

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    ne Intelligence]

    Machine learning is a rapidly growing field in the realm of artificial intelligence that focuses on creating algorithms and models that can learn from and make predictions or decisions based on data. In the fourth edition of “Introduction to Machine Learning” by Ethem Alpaydin, readers are introduced to the fundamental concepts and techniques of machine learning in a clear and accessible manner.

    This updated edition includes new material on deep learning, reinforcement learning, and other advanced topics, making it a comprehensive resource for those looking to delve deeper into the field. The book also covers practical applications of machine learning, such as image recognition, natural language processing, and robotics.

    Whether you are a beginner looking to understand the basics of machine learning or a seasoned professional seeking to expand your knowledge, “Introduction to Machine Learning” provides a solid foundation for understanding the principles and applications of this exciting field. With its emphasis on adaptive computation and machine intelligence, this edition is sure to be a valuable resource for anyone interested in the future of artificial intelligence.
    #Introduction #Machine #Learning #fourth #edition #Adaptive #Computation #Machi,machine learning: an applied mathematics introduction

  • Introduction to Statistical Relational Learning (Adaptive Computation and Maching Learning)


    Price: $29.73
    (as of Jan 19,2025 15:30:30 UTC – Details)




    Publisher ‏ : ‎ Mit Pr (January 1, 2007)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 586 pages
    ISBN-10 ‏ : ‎ 0262072882
    ISBN-13 ‏ : ‎ 978-0262072885
    Item Weight ‏ : ‎ 3.05 pounds
    Dimensions ‏ : ‎ 10.5 x 1.5 x 8.5 inches


    Statistical Relational Learning (SRL) is a subfield of machine learning that combines statistical methods with relational databases to model complex, structured data. This approach allows for the incorporation of relational information into the learning process, enabling the modeling of relationships between entities in a dataset.

    In the book “Introduction to Statistical Relational Learning” by Lise Getoor and Ben Taskar, readers are introduced to the fundamental concepts and techniques of SRL. The book covers topics such as probabilistic graphical models, relational learning, and probabilistic logic programming, providing a comprehensive overview of the field.

    One of the key strengths of SRL is its ability to handle data with complex relational structures, such as social networks, knowledge graphs, and biological networks. By capturing the dependencies between entities in a dataset, SRL can provide more accurate and interpretable models compared to traditional machine learning approaches.

    Overall, “Introduction to Statistical Relational Learning” is a valuable resource for researchers, practitioners, and students looking to delve into the exciting world of SRL. Whether you are interested in exploring the theoretical foundations of the field or applying SRL techniques to real-world problems, this book offers a solid foundation for understanding and implementing statistical relational learning algorithms.
    #Introduction #Statistical #Relational #Learning #Adaptive #Computation #Maching #Learning,machine learning: an applied mathematics introduction

  • Introduction to Machine Learning, fourth edition (Adaptive Computation and Mach,



    Introduction to Machine Learning, fourth edition (Adaptive Computation and Mach,

    Price : 74.99

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    In this post, we will be discussing the newly released fourth edition of “Introduction to Machine Learning” by Ethem Alpaydin. This book, part of the Adaptive Computation and Machine Learning series, provides a comprehensive introduction to the field of machine learning.

    The fourth edition of this book has been updated to include the latest developments in the field, including new algorithms, techniques, and applications. It covers a wide range of topics, from basic concepts such as supervised and unsupervised learning to more advanced topics like deep learning and reinforcement learning.

    Whether you are a student looking to learn the fundamentals of machine learning or a practitioner interested in keeping up with the latest advancements in the field, this book is a valuable resource. It is written in a clear and accessible manner, making it suitable for readers with varying levels of expertise in machine learning.

    Overall, “Introduction to Machine Learning, fourth edition” is a must-read for anyone interested in mastering the principles and techniques of machine learning. Grab your copy today and start exploring the exciting world of machine learning!
    #Introduction #Machine #Learning #fourth #edition #Adaptive #Computation #Mach,machine learning: an applied mathematics introduction

  • Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine



    Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine

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    Learning Series)

    In this post, we will explore the concept of machine learning from a probabilistic perspective, focusing on how it can be used to build adaptive computational systems. Machine learning is a branch of artificial intelligence that uses statistical techniques to enable computers to learn from data and make predictions or decisions without being explicitly programmed.

    One of the key aspects of machine learning is its reliance on probabilistic models to make inferences and predictions. By using probability theory, machine learning algorithms can handle uncertainty and make decisions based on the likelihood of different outcomes. This probabilistic perspective allows for more flexible and robust models that can adapt to new data and make accurate predictions in real-world scenarios.

    The Adaptive Computation and Machine Learning Series is a collection of books that delve into the theory and application of machine learning from a probabilistic perspective. These books cover topics such as Bayesian networks, graphical models, and reinforcement learning, providing a comprehensive overview of the field and its practical applications.

    Overall, a probabilistic perspective on machine learning offers a powerful framework for building adaptive computational systems that can learn from data and make intelligent decisions. By understanding the underlying principles of probability theory and how it can be applied to machine learning, we can unlock the full potential of this exciting field and drive innovation in artificial intelligence.
    #Machine #Learning #Probabilistic #Perspective #Adaptive #Computation #Machine,machine learning: an applied mathematics introduction

  • Introduction To Linear Algebra: Computation, Application, and Theory (Textbooks in Mathematics)


    Price: $33.46
    (as of Jan 18,2025 11:18:23 UTC – Details)




    ASIN ‏ : ‎ B09RKD5LSL
    Publisher ‏ : ‎ Chapman and Hall/CRC; 1st edition (February 22, 2022)
    Publication date ‏ : ‎ February 22, 2022
    Language ‏ : ‎ English
    File size ‏ : ‎ 13442 KB
    Simultaneous device usage ‏ : ‎ Up to 4 simultaneous devices, per publisher limits
    Text-to-Speech ‏ : ‎ Not enabled
    Enhanced typesetting ‏ : ‎ Not Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Format ‏ : ‎ Print Replica


    Introduction To Linear Algebra: Computation, Application, and Theory (Textbooks in Mathematics)

    Are you looking to delve into the world of linear algebra and gain a solid understanding of its computation, applications, and theoretical foundations? Look no further than this comprehensive textbook, designed for students and professionals alike.

    This book covers a wide range of topics, from basic vector spaces and matrix operations to advanced concepts such as eigenvectors, diagonalization, and inner product spaces. Each chapter is carefully crafted to provide clear explanations, worked examples, and exercises to reinforce your understanding.

    Whether you are studying for a mathematics course, preparing for a career in data science, or simply interested in learning more about linear algebra, this textbook is the perfect resource to guide you through the fundamentals and beyond.

    Don’t miss out on this essential tool for mastering linear algebra. Get your copy today and start exploring the beauty and power of this versatile mathematical discipline.
    #Introduction #Linear #Algebra #Computation #Application #Theory #Textbooks #Mathematics,machine learning: an applied mathematics introduction

  • Introduction to Online Convex Optimization, second edition (Adaptive Computation and Machine Learning series)

    Introduction to Online Convex Optimization, second edition (Adaptive Computation and Machine Learning series)


    Price: $60.00
    (as of Jan 04,2025 15:59:03 UTC – Details)




    Publisher ‏ : ‎ The MIT Press; 2nd edition (September 6, 2022)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 248 pages
    ISBN-10 ‏ : ‎ 0262046989
    ISBN-13 ‏ : ‎ 978-0262046985
    Item Weight ‏ : ‎ 2.31 pounds
    Dimensions ‏ : ‎ 6.19 x 0.66 x 9.25 inches


    Online Convex Optimization is a powerful framework for solving optimization problems in a dynamic and evolving environment. The second edition of the book “Introduction to Online Convex Optimization” in the Adaptive Computation and Machine Learning series is a comprehensive guide to this important area of machine learning.

    In this book, the authors provide a thorough introduction to the fundamental concepts and techniques of online convex optimization. They cover topics such as regret analysis, online mirror descent, and online gradient descent, as well as more advanced topics like online learning with expert advice and bandit problems.

    The second edition of “Introduction to Online Convex Optimization” has been extensively revised and updated to reflect the latest research and developments in the field. It includes new material on topics like online learning with constraints, online learning with partial feedback, and online learning in non-Euclidean spaces.

    Whether you are a student, researcher, or practitioner in the field of machine learning, this book is essential reading for anyone interested in understanding and applying online convex optimization techniques. With its clear explanations, practical examples, and comprehensive coverage of the subject, “Introduction to Online Convex Optimization” is sure to become a valuable resource for anyone working in this exciting and rapidly evolving field.
    #Introduction #Online #Convex #Optimization #edition #Adaptive #Computation #Machine #Learning #series,machine learning: an applied mathematics introduction

  • Introduction to Machine Learning (Adaptive Computation and Machine Learning), Al

    Introduction to Machine Learning (Adaptive Computation and Machine Learning), Al



    Introduction to Machine Learning (Adaptive Computation and Machine Learning), Al

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    Introduction to Machine Learning (Adaptive Computation and Machine Learning)

    Machine learning is a rapidly growing field that intersects computer science and statistics to enable computers to learn from data. In the book “Introduction to Machine Learning” by Ethem Alpaydin, readers are introduced to the fundamental concepts and techniques of machine learning.

    The book covers various topics such as supervised and unsupervised learning, reinforcement learning, neural networks, and support vector machines. It also delves into the practical aspects of machine learning, including feature selection, model evaluation, and overfitting.

    With a focus on both theoretical understanding and practical applications, “Introduction to Machine Learning” provides a comprehensive introduction to the field. Whether you are a beginner looking to learn the basics of machine learning or a seasoned practitioner looking to deepen your knowledge, this book is a valuable resource for anyone interested in this exciting and rapidly evolving field.
    #Introduction #Machine #Learning #Adaptive #Computation #Machine #Learning,machine learning: an applied mathematics introduction

  • Machine Learning: A Probabilistic Perspective [Adaptive Computation and Machine

    Machine Learning: A Probabilistic Perspective [Adaptive Computation and Machine



    Machine Learning: A Probabilistic Perspective [Adaptive Computation and Machine

    Price : 44.32

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    Learning Series]

    Machine learning is a rapidly growing field that has the potential to revolutionize industries ranging from healthcare to finance. One of the key approaches in machine learning is the probabilistic perspective, which involves using probability theory to model uncertainty and make predictions.

    In the book “Machine Learning: A Probabilistic Perspective,” authors Kevin P. Murphy and Christopher M. Bishop provide a comprehensive overview of the probabilistic approach to machine learning. The book covers topics such as Bayesian networks, Gaussian processes, and hidden Markov models, offering a thorough introduction to the principles and techniques of probabilistic machine learning.

    By incorporating probabilistic models into machine learning algorithms, researchers and practitioners can improve the accuracy and reliability of their predictions. Probabilistic models allow for uncertainty to be quantified and accounted for, providing a more robust framework for decision-making.

    Overall, the probabilistic perspective offers a powerful way to approach machine learning problems, enabling researchers to build more flexible and adaptable models. If you’re interested in delving deeper into the probabilistic side of machine learning, “Machine Learning: A Probabilistic Perspective” is a valuable resource to add to your library.
    #Machine #Learning #Probabilistic #Perspective #Adaptive #Computation #Machine,machine learning: an applied mathematics introduction

  • Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine

    Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine



    Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine

    Price : 55.47

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    Learning Series)

    In this post, we will delve into the world of machine learning from a probabilistic perspective. Machine learning is a powerful tool that has revolutionized various industries, from healthcare to finance to transportation. By understanding the probabilistic foundations of machine learning, we can gain deeper insights into how algorithms make decisions and predictions.

    The book “Machine Learning: A Probabilistic Perspective” by Kevin P. Murphy is a comprehensive resource that covers the fundamental concepts of machine learning through a probabilistic lens. The book is part of the Adaptive Computation and Machine Learning series, which focuses on providing a rigorous and practical understanding of machine learning techniques.

    One of the key insights of the probabilistic approach to machine learning is the emphasis on uncertainty. In traditional machine learning models, predictions are often made with a high degree of certainty, leading to overfitting and poor generalization. By incorporating probabilistic reasoning, machine learning algorithms can capture the inherent uncertainty in data and make more robust predictions.

    The book covers a wide range of topics, including Bayesian inference, graphical models, and probabilistic graphical models. These concepts provide a solid foundation for understanding complex machine learning algorithms and their applications in real-world scenarios.

    Overall, “Machine Learning: A Probabilistic Perspective” offers a comprehensive and accessible introduction to the probabilistic foundations of machine learning. Whether you are a beginner or an experienced practitioner, this book is a valuable resource for anyone looking to deepen their understanding of machine learning algorithms and techniques.
    #Machine #Learning #Probabilistic #Perspective #Adaptive #Computation #Machine,machine learning: an applied mathematics introduction