Mathematical Theories of Machine Learning - Theory and Applications

Discover the intricate world of machine learning with "Mathematical Theories of Machine Learning - Theory and Applications" by Bin Shi. Published by Springer Nature Switzerland AG in 2020, this insightful paperback spans 133 pages, delving into advanced mathematical concepts that underpin machine learning technologies.

In this comprehensive volume, the author explores subspace clustering, addressing the challenges posed by noisy and incomplete data. This topic is particularly relevant for those working with real-world data influenced by stochastic Gaussian noise and missing entries. Bin Shi's expertise provides readers with a solid foundation in both theory and practical applications, making this book an essential resource for students, researchers, and professionals in the field.

Enhance your understanding of machine learning with this essential guide that bridges the gap between theoretical mathematics and practical machine learning applications.