Foundations of Machine Learning, second edition (Adaptive Computation and Machine Learning series)

### Editorial Review "Foundations of Machine Learning, second edition," is an academic text that thoroughly covers the foundational aspects of statistical machine learning. It is highly regarded by those with a strong mathematical background who appreciate the rigorous treatment of the subject. Customers have voiced their satisfaction with the second edition, noting that it serves as an excellent reference and supplement to their academic pursuits. The book delves deeply into theoretical concepts, which some readers find indispensable for a deeper understanding of the mechanics behind machine learning algorithms. However, this book is not for everyone. It is targeted at readers who already understand key mathematical principles, such as probability and linear algebra. Those new to machine learning may struggle with the book's dense mathematical language and proofs, making it less suitable as a standalone introduction to the field. Moreover, individuals looking for practical, hands-on approaches or coverage of deep learning concepts will find this book lacking, as its focus is primarily on theoretical foundations rather than applications. Despite its high-quality content, some readers have noted the absence of programming examples, such as implementations in popular libraries like Keras, which diminishes its utility for applied machine learning tasks. The lack of a comprehensive solution manual for exercises may also deter self-learners who seek guidance. Overall, "Foundations of Machine Learning" is recommended for those who possess a robust mathematical background and are ready to engage with complex theories. Those who wish to learn practical machine learning applications might Consider pairing this book with more hands-on resources. #### Pros and Cons **