علوم داده و یادگیری ماشین؛ روشهای ریاضی و آماری
قیمت 16,000 تومان
سال انتشار: 2020 | تعداد صفحات: 532 | حجم فایل: 14.57 مگابایت | زبان: انگلیسی
Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/Crc Machine Learning & Pattern Recognition)
Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman
"This textbook is a well-rounded, rigorous, and informative work presenting the mathematics behind modern machine learning techniques. It hits all the right notes: the choice of topics is up-to-date and perfect for a course on data science for mathematics students at the advanced undergraduate or early graduate level. This book fills a sorely-needed 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 well-written book that provides a deeper dive into data-scientific 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 College
The 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.
Focuses on mathematical understanding.
Presentation is self-contained, accessible, and comprehensive.
Extensive list of exercises and worked-out examples.
Many concrete algorithms with Python code.
Full color throughout.