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Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/CRC Machine Learning & Pattern Recogniti,Used
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This textbook is a wellrounded, rigorous, and informative work presenting the mathematics behind modern machine learning techniques. It hits all the right notes: the choice of topics is uptodate and perfect for a course on data science for mathematics students at the advanced undergraduate or early graduate level. This book fills a sorelyneeded gap in the existing literature by not sacrificing depth for breadth, presenting proofs of major theorems and subsequent derivations, as well as providing a copious amount of Python code. I only wish a book like this had been around when I first began my journey!' Nicholas Hoell, University of Toronto'This is a wellwritten book that provides a deeper dive into datascientific methods than many introductory texts. The writing is clear, and the text logically builds up regularization, classification, and decision trees. Compared to its probable competitors, it carves out a unique niche. Adam Loy, Carleton CollegeThe purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.Key Features: Focuses on mathematical understanding. Presentation is selfcontained, accessible, and comprehensive. Extensive list of exercises and workedout examples. Many concrete algorithms with Python code. Full color throughout.Further Resources can be found on the authors website: https://github.com/DSMLbook/Lectures
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