The book "Introduction to Machine Learning Theory" (hereinafter referred to as "Introduction") written by Zhou Zhihua, Wang Wei, Gao Wei, and other teachers fills the regret of the lack of introductory works on machine learning theory in China. This book attempts to provide an introductory guide for readers interested in learning machine learning theory and researching machine learning theory in an easy-to-understand language. "Guide" mainly covers seven parts, corresponding to seven important concepts or theoretical tools in machine learning theory, namely: learnability, (hypothesis space) complexity, generalization bound, stability, consistency, convergence rate, regret circle. Daoyin is a highly theoretical book, involving a large number of mathematical theorems and various proofs. Although the writing team has reduced the difficulty as much as possible, due to the nature of machine learning theory, the book still places high demands on the reader's mathematical background.
Features
- Proof Supplement : Explain the proof idea of the partial proof, and supplement the partially omitted proof process
- Case Supplement : Add explanation cases to help readers understand
- Concept Supplement : Introduce some concepts involved but not explained in the text
- Explanation of References : Introduce some important references
- The content of the "Key Book" starts from the second chapter of the "Daoyin"
- Attempts to provide an introductory guide for readers interested in learning machine learning theory
Project Samples
