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Theoretic Foundation of Predictive Data Analytics

  • ID: 3744804
  • Book
  • October 2017
  • 256 Pages
  • Elsevier Science and Technology
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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.

  • 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
  • Illustrates 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
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1. Probability Theory and LLN 2. Maximum Likelihood Estimation 3. Linear Regression 4. Ridge Regression 5. Linear Classification 6. Akaike Information Criterion (AIC) 7. Support Vector Machines 8. Statistical Learning Theory 9. Statistical Decision Theory 10. Exchangeability 11. Bayesian Linear Regression 12. Gaussian Process 13. Ensemble learning 14. Optimization

A Real Number and Vector Space B Vector Space C Advanced Probability and SLLN

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Huan, Jun (Luke)
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

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