Mathematics for Machine Learning

Rezensionen / Stimmen

'This book provides great coverage of all the basic mathematical concepts for machine learning. I'm looking forward to sharing it with students, colleagues, and anyone interested in building a solid understanding of the fundamentals.' Joelle Pineau, McGill University, Montreal 'The field of machine learning has grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This comprehensive text covers the key mathematical concepts that underpin modern machine learning, with a focus on linear algebra, calculus, and probability theory. It will prove valuable both as a tutorial for newcomers to the field, and as a reference text for machine learning researchers and engineers.' Christopher Bishop, Microsoft Research Cambridge 'This book provides a beautiful exposition of the mathematics underpinning modern machine learning. Highly recommended for anyone wanting a one-stop-shop to acquire a deep understanding of machine learning foundations.' Pieter Abbeel, University of California, Berkeley 'Really successful are the numerous explanatory illustrations, which help to explain even difficult concepts in a catchy way. Each chapter concludes with many instructive exercises. An outstanding feature of this book is the additional material presented on the website ...' Volker H. Schulz, SIAM Review 'A solid and affordable resource for building foundational skills in machine learning.' Ruwan Karunanayaka, University of the Fraser Valley 'This is an excellent book on the mathematical foundations of machine learning. The colourful figures and even some equations make the content both engaging and easy to follow. I will definitely be recommending this book to my students.' Hom Nath Gharti, Queen's University