Self-contained, easy-to-follow content enables readers to learn and solve exercises without prior experience
Includes 100 carefully selected exercises with solutions in the text for reinforced learning
Proven mathematical premises and clear conclusions help readers understand kernel theory
Source code and running examples provide hands-on experience with kernel methods
Covers both reproducing kernel Hilbert space and Gaussian process kernels for a complete foundation
Summarized by Shop
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building Python programs. The book’s main features are as