Introduction To Machine Learning In Quantitative Finance An Advanced Textbooks In Mathematics(1st Edition)
Authors:
Hao Ni, Xin Dong, Jinsong Zheng, Guangxi Yu
In Stock: 1 Left
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$0
Solution Manual Includes
Price:
$9.99
Book details
ISBN: 1786349361, 9781786349361
Book publisher: WSPC
Book Price $0 : The textbook "Introduction to Machine Learning in Quantitative Finance" by Hao Ni, Xin Dong, Jinsong Zheng, and Guangxi Yu is a definitive resource for understanding the intersection of machine learning and quantitative finance. The book intricately explores advanced mathematics concepts that are vital in these fields, providing readers with a detailed theoretical framework and practical applications. It covers key machine learning methodologies such as supervised and unsupervised learning, neural networks, and reinforcement learning, all tailored to solve complex quantitative finance problems. The authors have ensured that the text serves as a comprehensive solution manual, offering an answer key for complex mathematical problems presented throughout the chapters. This textbook is structured with a detailed table of contents that guides the reader through foundational topics up to advanced applications in quantitative finance. Students and professionals alike will find the book invaluable for its rigorous approach and its ability to bridge theoretical knowledge with practical implementation using programming languages such as Python and R. The reception of this publication highlights its significance in providing a comprehensive educational resource that aligns complex mathematical theories with contemporary machine learning applications in finance. In essence, it is both an educational and practical compendium for those looking to delve deeply into quantitative finance with machine learning tools. With cheap study aids included, this summary serves as a valuable companion for exam preparation.
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