Jun (Luke) Huan - Theoretical Foundation of Data Science download MOBI, DJV, TXT
9780128036808 English 012803680X Data science is penetrating into virtually every discipline of science, engineering, and medicine. The field is fast evolving. Practitioners, researchers and graduate students often have difficulty in understanding the foundation of data science. In order to have a deep understanding of data science, one must first have a strong understanding of statistical analysis and machine learning. Theoretical Foundations of Machine Learning and Statistical introduces commonly used statistical principles behind many machine learning and data mining algorithms, the connections of those principles, and the connections of those principles to commonly utilized data analytic algorithms. Drawing upon years of practical experience and using numerous examples and use cases, Professor Juan discusses: A comprehensive and concise description of statistical principles behind many data analytics algorithms. The connection of widely used data analytics methods and the statistical and computational principles. Applied examples from several disciplines including not limited to; Bioinformatics, health informatics, Social Networks and Engineering. Extensive experimental results using real application data sets to demonstrate the performance of statistical and machine learning techniques. Provides a comprehensive and concise description of statistical principles behind many data analytics algorithms. Illustrates the connection of widely used data analytics methods and the statistical and computational principles. Ideal for readers that want to go deep into the basics of statistics and probability and how it applies to data science. Presents applied examples from several disciplines including not limited to; computer science, engineering and medicine. Discusses extensive experimental results using real application data sets to demonstrate the performance of statistical and machine learning techniques., Theoretical Foundation of Data Science presents the latest in data science, an area that is penetrating into virtually every discipline of science, engineering, and medicine, and is a fast evolving field. Practitioners, researchers, and graduate students often have difficulty in understanding the foundation of data science. In order to have a deep understanding of data science, a strong understanding of statistical analysis and machine learning is a must. This book introduces the commonly used statistical principles behind many machine learning and data mining algorithms, the connections of those principles, and the connections of those principles to commonly utilized data analytic algorithms. Presents an ideal guide for readers that want to go deep into the basics of statistics and probability and how it applies to data science llustrates the connection of widely used data analytics methods and statistical and computational principles Presents applied examples from several disciplines including, but not limited to, computer science, engineering and medicine Discusses extensive experimental results using real application datasets to demonstrate the performance of statistical and machine learning techniques
9780128036808 English 012803680X Data science is penetrating into virtually every discipline of science, engineering, and medicine. The field is fast evolving. Practitioners, researchers and graduate students often have difficulty in understanding the foundation of data science. In order to have a deep understanding of data science, one must first have a strong understanding of statistical analysis and machine learning. Theoretical Foundations of Machine Learning and Statistical introduces commonly used statistical principles behind many machine learning and data mining algorithms, the connections of those principles, and the connections of those principles to commonly utilized data analytic algorithms. Drawing upon years of practical experience and using numerous examples and use cases, Professor Juan discusses: A comprehensive and concise description of statistical principles behind many data analytics algorithms. The connection of widely used data analytics methods and the statistical and computational principles. Applied examples from several disciplines including not limited to; Bioinformatics, health informatics, Social Networks and Engineering. Extensive experimental results using real application data sets to demonstrate the performance of statistical and machine learning techniques. Provides a comprehensive and concise description of statistical principles behind many data analytics algorithms. Illustrates the connection of widely used data analytics methods and the statistical and computational principles. Ideal for readers that want to go deep into the basics of statistics and probability and how it applies to data science. Presents applied examples from several disciplines including not limited to; computer science, engineering and medicine. Discusses extensive experimental results using real application data sets to demonstrate the performance of statistical and machine learning techniques., Theoretical Foundation of Data Science presents the latest in data science, an area that is penetrating into virtually every discipline of science, engineering, and medicine, and is a fast evolving field. Practitioners, researchers, and graduate students often have difficulty in understanding the foundation of data science. In order to have a deep understanding of data science, a strong understanding of statistical analysis and machine learning is a must. This book introduces the commonly used statistical principles behind many machine learning and data mining algorithms, the connections of those principles, and the connections of those principles to commonly utilized data analytic algorithms. Presents an ideal guide for readers that want to go deep into the basics of statistics and probability and how it applies to data science llustrates the connection of widely used data analytics methods and statistical and computational principles Presents applied examples from several disciplines including, but not limited to, computer science, engineering and medicine Discusses extensive experimental results using real application datasets to demonstrate the performance of statistical and machine learning techniques