Data Science and Machine Learning

ISBN

1138492531

“This textbook is a well-rounded, rigorous, and informative work presenting the mathematics behind modern algorithms for machine learning. It hits all of the accurate notes: the variety of topics is up-to-date and exceptional for a course on statistics for mathematics students on the advanced undergraduate or early graduate level. This book fills a sorely-needed gap within the current literature by not sacrificing depth for breadth, presenting proofs of major theorems and subsequent derivations, along with providing a copious amount of Python code. I solely need a book like this had been around when I first started my journey!” -Nicholas Hoell, School of Toronto “This could be a well-written book that offers a deeper dive into data-scientific methods than many introductory texts. The writing is apparent, and the text logically builds up regularization, classification, and decision trees. Compared with its possible competitors, it carves out a unique niche. -Adam Loy, Carleton College The purpose of Statistics and Artificial Intelligence: Mathematical and Statistical Methods is to supply an accessible, however full textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of concepts and machine learning algorithms in statistics. Key Features: Focuses on mathematical understanding. Presentation is self-contained, accessible, and full. Extensive listing of exercises and worked-out examples. Many concrete algorithms with Python code. Full color throughout.   Additional Resources may be found on the authors