Theoretic Foundation of Predictive Data Analytics
Will be published approx. on 1. October 2029
Book
Paperback/Softback
256 pages
978-0-12-803655-6 (ISBN)
Description
Theoretic Foundation of Predictive Data Analytics 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.
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.
More details
Language
English
Place of publication
San Francisco
United States
Publishing group
Elsevier Science & Technology
Target group
College/higher education
Professional and scholarly
Dimensions
Height: 235 mm
Width: 191 mm
ISBN-13
978-0-12-803655-6 (9780128036556)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
Professor Jun Huan, Ph.D. is a Professor in the Department of Electrical Engineering and Computer Science at the University of Kansas. He directs the Bioinformatics and Computational Life Sciences Laboratory at KU Information and Telecommunication Technology Center (ITTC). Dr. Huan works on data science, machine learning, data mining, big data, and interdisciplinary topics including bioinformatics. Dr. Huan serves the editorial board of several international journals including the Springer Journal of Big Data, Elsevier Journal of Big Data Research, and the International Journal of Data Mining and Bioinformatics. He regularly serves on the program committees of top-tier international conferences on machine learning, data mining, big data, and bioinformatics
Content
1. Probability Theory and LLN2. Maximum Likelihood Estimation3. Linear Regression4. Ridge Regression5. Linear Classification6. Akaike Information Criterion (AIC)7. Support Vector Machines8. Statistical Learning Theory9. Statistical Decision Theory10. Exchangeability11. Bayesian Linear Regression12. Gaussian Process13. Ensemble learning14. Optimization
A Real Number and Vector SpaceB Vector SpaceC Advanced Probability and SLLN
A Real Number and Vector SpaceB Vector SpaceC Advanced Probability and SLLN