Description
This Chapman & Hall publication provides a rigorous treatment of the mathematical and statistical methods underlying data science and machine learning. Written by leading experts including Zdravko Botev and Dirk P. Kroese, the book bridges theory and practice by presenting fundamental concepts with practical applications.
The text covers probability theory, statistical inference, optimization techniques, and machine learning algorithms from first principles. It emphasizes the mathematical rigor necessary to understand how and why machine learning models work, making it ideal for practitioners seeking deeper theoretical knowledge. Topics include supervised and unsupervised learning, statistical testing, and computational methods essential for modern data science applications.
Suitable for graduate students, researchers, and professionals in data science, this book serves as both a reference and a learning resource for those building expertise in machine learning foundations.
Reviews
There are no reviews yet.