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- Introduction to Statistical Machine Learning

- Author : Masashi Sugiyama
- Publsiher : Morgan Kaufmann Publishers
- Release : 12 October 2015
- ISBN : 9780128021217
- Pages : 534 pages
- Rating : /5 from reviews

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Download or read book entitled *Introduction to Statistical Machine Learning* by author: *Masashi Sugiyama* which was release on *12 October 2015* and published by *Morgan Kaufmann Publishers* with total page 534 pages . This book available in PDF, EPUB and Kindle Format. Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks. Provides the necessary background material to understand machine learning such as statistics, probability, linear algebra, and calculus. Complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning. Includes MATLAB/Octave programs so that readers can test the algorithms numerically and acquire both mathematical and practical skills in a wide range of data analysis tasks Discusses a wide range of applications in machine learning and statistics and provides examples drawn from image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials.

- Author : Masashi Sugiyama
- Publisher : Morgan Kaufmann Publishers
- Release Date : 2015-10-12
- Total pages : 534
- ISBN : 9780128021217

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**Summary :** Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as ...

- Author : Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani
- Publisher : Springer Science & Business Media
- Release Date : 2013-06-24
- Total pages : 426
- ISBN : 9780128021217

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**Summary :** An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents ...

- Author : Daniel D. Gutierrez
- Publisher : Technics Publications
- Release Date : 2015-11-01
- Total pages : 282
- ISBN : 9780128021217

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**Summary :** A practitioner’s tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised ...

- Author : Himanshu Singh
- Publisher : BPB Publications
- Release Date : 2021-01-15
- Total pages : 278
- ISBN : 9780128021217

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**Summary :** A practical guide that will help you understand the Statistical Foundations of any Machine Learning Problem KEY FEATURES ● Develop a Conceptual and Mathematical understanding of Statistics ● Get an overview of Statistical Applications in Python ● Learn how to perform Hypothesis testing in Statistics ● Understand why Statistics is important in Machine Learning ● ...

- Author : Masashi Sugiyama
- Publisher : Morgan Kaufmann
- Release Date : 2015-10-31
- Total pages : 534
- ISBN : 9780128021217

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**Summary :** Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as ...

- Author : Sanjeev Kulkarni,Gilbert Harman
- Publisher : John Wiley & Sons
- Release Date : 2011-06-09
- Total pages : 288
- ISBN : 9780128021217

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**Summary :** A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of ...

- Author : Masashi Sugiyama
- Publisher : CRC Press
- Release Date : 2015-03-16
- Total pages : 206
- ISBN : 9780128021217

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**Summary :** 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 decision making in unknown environments with large amo...

- Author : Richard Golden
- Publisher : CRC Press
- Release Date : 2020-06-24
- Total pages : 506
- ISBN : 9780128021217

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**Summary :** The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to ...

- Author : Laura Igual,Santi Seguí
- Publisher : Springer
- Release Date : 2017-02-22
- Total pages : 218
- ISBN : 9780128021217

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**Summary :** This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks ...

- Author : Gopinath Rebala,Ajay Ravi,Sanjay Churiwala
- Publisher : Springer
- Release Date : 2019-05-07
- Total pages : 263
- ISBN : 9780128021217

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**Summary :** Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview ...

- Author : Lise Getoor,Ben Taskar
- Publisher : MIT Press
- Release Date : 2019-09-22
- Total pages : 608
- ISBN : 9780128021217

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**Summary :** Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications. Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from ...

- Author : Xiaojin Zhu,Andrew B. Goldberg
- Publisher : Morgan & Claypool Publishers
- Release Date : 2009
- Total pages : 116
- ISBN : 9780128021217

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**Summary :** Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, ...

- Author : Christopher M. Bishop
- Publisher : Springer Verlag
- Release Date : 2006-08-17
- Total pages : 738
- ISBN : 9780128021217

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**Summary :** This is the first text on pattern recognition to present the Bayesian viewpoint, one that has become increasing popular in the last five years. It presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It provides the first text to use graphical ...

- Author : Ethem Alpaydin
- Publisher : MIT Press
- Release Date : 2004
- Total pages : 415
- ISBN : 9780128021217

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**Summary :** An introductory text in machine learning that gives a unified treatment of methods based on statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining....

- Author : Ke-Lin Du,M. N. S. Swamy
- Publisher : Springer Science & Business Media
- Release Date : 2013-12-09
- Total pages : 824
- ISBN : 9780128021217

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**Summary :** Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop ...