Random Matrix Methods for Machine Learning
by Romain Couillet, Zhenyu Liao
This book introduces random matrix theory and demonstrates its essential role in understanding and improving machine learning methods for high-dimensional data.
About This Book
Random Matrix Methods for Machine Learning explores the mathematical foundations of random matrix theory and its relevance to data science and machine learning.
The book provides tools and techniques for analyzing large-dimensional data using random matrix models.
Readers will gain insight into the theoretical underpinnings that support many contemporary machine learning algorithms.
Authored by Romain Couillet and Zhenyu Liao, the text bridges advanced mathematics with practical applications in data analysis.
Reviews
No reviews yet. Be the first to review this book!