Decision Mathematics Statistical Learning And Data Mining
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Decision Mathematics Statistical Learning And Data Mining
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Author : Wan Fairos Wan Yaacob
language : en
Publisher: Springer Nature
Release Date : 2024-10-26
Decision Mathematics Statistical Learning And Data Mining written by Wan Fairos Wan Yaacob and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-26 with Mathematics categories.
This book is a collection of selected research papers presented at the Mathematics, Statistics and Computing Technology (ICMSCT2023), held at the UST Angelicum College, Philippines, from 20th to 21st September 2023. This biennial event is a result from collaborations of university partners in Malaysia, Thailand, Indonesia and Philippines. Increasing investment in digital technologies is a challenge faced by most countries after the crisis caused by COVID-19 and the demand of technological revolution 4.0. Indirectly, regardless of their level of development, they take into account the importance of redesigning strategies for resilient and sustainable regional economic development, increasing regional resilience and minimizing recovery costs as a basis for development. In such situation, this book gather discussion, viewpoints and findings on the recent works of mathematical and computing technology applications in order to propose solutions to overcome adversity of digital resilience. This book covers a wide range of topics on applied mathematics, which includes decision mathematics and also applied statistics covering statistical learning with applications. In addition, the book also highlight the latest application of statistical mining and data visualization, particularly on data mining, machine learning and data visualization. Editors believe this book will interest and influence researchers on the recent techniques, methodologies and applications to ensure digital resilience and support future research.
Statistical Learning With Math And Python
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Author : Joe Suzuki
language : en
Publisher: Springer Nature
Release Date : 2021-08-03
Statistical Learning With Math And Python written by Joe Suzuki and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-03 with Computers categories.
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs. As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning. Each chapter mathematically formulates and solves machine learning problems and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter. Because the book is carefully organized to provide the solutions to the exercises in each chapter, readers can solve the total of 100 exercises by simply following the contents of each chapter. This textbook is suitable for an undergraduate or graduate course consisting of about 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.
The Elements Of Statistical Learning
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Author : Trevor Hastie
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11
The Elements Of Statistical Learning written by Trevor Hastie and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Mathematics categories.
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.
Principles And Theory For Data Mining And Machine Learning
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Author : Bertrand Clarke
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-07-21
Principles And Theory For Data Mining And Machine Learning written by Bertrand Clarke and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-21 with Computers categories.
Extensive treatment of the most up-to-date topics Provides the theory and concepts behind popular and emerging methods Range of topics drawn from Statistics, Computer Science, and Electrical Engineering
Mathematics For Machine Learning
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Author : Marc Peter Deisenroth
language : en
Publisher: Cambridge University Press
Release Date : 2020-04-23
Mathematics For Machine Learning written by Marc Peter Deisenroth and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-23 with Computers categories.
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
The Elements Of Statistical Learning
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Author : Trevor Hastie
language : en
Publisher:
Release Date : 2009
The Elements Of Statistical Learning written by Trevor Hastie and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Biology categories.
Data Science For Mathematicians
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Author : Nathan Carter
language : en
Publisher: CRC Press
Release Date : 2020-09-15
Data Science For Mathematicians written by Nathan Carter and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-15 with Mathematics categories.
Mathematicians have skills that, if deepened in the right ways, would enable them to use data to answer questions important to them and others, and report those answers in compelling ways. Data science combines parts of mathematics, statistics, computer science. Gaining such power and the ability to teach has reinvigorated the careers of mathematicians. This handbook will assist mathematicians to better understand the opportunities presented by data science. As it applies to the curriculum, research, and career opportunities, data science is a fast-growing field. Contributors from both academics and industry present their views on these opportunities and how to advantage them.
Statistical Decision Theory
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Author : F. Liese
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-12-30
Statistical Decision Theory written by F. Liese and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-12-30 with Mathematics categories.
For advanced graduate students, this book is a one-stop shop that presents the main ideas of decision theory in an organized, balanced, and mathematically rigorous manner, while observing statistical relevance. All of the major topics are introduced at an elementary level, then developed incrementally to higher levels. The book is self-contained as it provides full proofs, worked-out examples, and problems. The authors present a rigorous account of the concepts and a broad treatment of the major results of classical finite sample size decision theory and modern asymptotic decision theory. With its broad coverage of decision theory, this book fills the gap between standard graduate texts in mathematical statistics and advanced monographs on modern asymptotic theory.
Machine Intelligence And Soft Computing
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Author : Debnath Bhattacharyya
language : en
Publisher: Springer
Release Date : 2021-01-21
Machine Intelligence And Soft Computing written by Debnath Bhattacharyya and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-21 with Technology & Engineering categories.
This book gathers selected papers presented at the International Conference on Machine Intelligence and Soft Computing (ICMISC 2020), held jointly by Vignan’s Institute of Information Technology, Visakhapatnam, India and VFSTR Deemed to be University, Guntur, AP, India during 03-04 September 2020. Topics covered in the book include the artificial neural networks and fuzzy logic, cloud computing, evolutionary algorithms and computation, machine learning, metaheuristics and swarm intelligence, neuro-fuzzy system, soft computing and decision support systems, soft computing applications in actuarial science, soft computing for database deadlock resolution, soft computing methods in engineering, and support vector machine.
Foundations Of Data Science
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Author : Avrim Blum
language : en
Publisher: Cambridge University Press
Release Date : 2020-01-23
Foundations Of Data Science written by Avrim Blum and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-23 with Computers categories.
Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.