Machine Learning: An Algorithmic Perspective, Second Edition (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

*Machine Learning: An Algorithmic Perspective, Second Edition* offers a practical, hands-on approach designed specifically for computer science students who may lack a robust background in statistics. Recognizing the growing importance of statistical interpretations in the field, this updated text bridges the gap by guiding readers through the essential algorithms of machine learning while simultaneously building their understanding of the underlying mathematics. The book serves as a comprehensive resource for mastering both the theoretical concepts and the necessary programming skills required for experimentation, making complex topics accessible to those starting without a strong statistical foundation. This second edition introduces significant updates to reflect recent developments in the field, including two entirely new chapters focused on deep belief networks and Gaussian processes. The content has been reorganized for a more natural flow, and existing material on support vector machines has been revised to include a simple implementation for practical experiments. Readers will also find new discussions on random forests, the perceptron convergence theorem, and advanced optimization techniques like conjugate gradient optimization for multi-layer perceptrons. Additionally, the text expands on Kalman and particle filters, ensuring a thorough coverage of modern machine learning techniques. Suitable for both introductory one-semester courses and more advanced study, the book strongly emphasizes active learning through coding and practice. Each chapter features detailed examples, problem sets, and suggestions for further reading to reinforce the material. To support this hands-on philosophy, the author has improved the accompanying Python code with better naming conventions and made all code used in the examples available on a dedicated website. This combination of clear algorithmic explanations and practical application provides a solid pathway for students to gain proficiency in machine learning.