علوم داده و یادگیری ماشین؛ روش‌های ریاضی و آماری

دسته: پایگاه داده‌ها و SQL، علوم کامپیوتر
علوم داده و یادگیری ماشین؛ روش‌های ریاضی و آماری

سال انتشار: 2020  |  532 صفحه  |  حجم فایل: 15 مگابایت  |  زبان: انگلیسی

Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/Crc Machine Learning & Pattern Recognition)
نویسنده:
Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman
ناشر:
CRC Press
ISBN10:
1138492531
ISBN13:
9781138492530

 

قیمت: 8000 تومان

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کتابهای مرتبط:

"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. Key Features: 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.


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