Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels
'... one of the first books describing machine learning techniques in the context of environmental applications ... goes a long way in explaining these subjects in a very clear, concise, and understandable way. This is one of the few books where one will find diverse areas of machine learning all within the same cover ... aimed at advanced undergraduates and PhD students, as well as researchers and practitioners. No previous knowledge of machine learning concepts is assumed.' Vladimir Krasnopolsky, National Oceanic and Atmospheric Administration (NOAA) and National Weather Service'[This book] aims to, and succeeds in, bridging the gap between AI and what is often referred to as conventional statistics. Add to that the unique perspective that a physicist and an environmental scientist brings to the table, and one has a truly rare book. ... a well-balanced mix of theoretical and practical exercises. ... Hsieh's book [is] ideal as both a textbook on the topic, and a reference book for the researcher in the field.' Caren Marzban, University of Washington and University of Oklahoma'The material is explained in a straightforward, clear, concise, and complete manner. The reader does not have to wade through lengthy explanations and can proceed quickly. All relevant topics are covered from historical to very recent. The full mathematical equations are presented for every topic so the reader may fully appreciate the theory and concepts discussed. Numerous diagrams are included, and are of great utility for explaining complex material and concepts. All of the main facets of machine learning are covered, including theory, data selection, data reduction and data clustering, and problems of overfitting and underfitting data.This book is unique because it presents machine learning in the context of environmental science applications. I found it to be a valuable tool to bring myself up-to-date with the historical and recent developments in the subject of machine learning, and I believe the reader will too. The purchase price is modest. I highly recommend that any student or researcher interested in machine learning methods obtain a copy.' The material is explained in a straightforward, clear, concise, and complete manner. The reader does not have to wade through lengthy explanations and can proceed quickly. All relevant topics are covered from historical to very recent. The full mathematical equations are presented for every topic so the reader may fully appreciate the theory and concepts discussed. Numerous diagrams are included, and are of great utility for explaining complex material and concepts. All of the main facets of machine learning are covered, including theory, data selection, data reduction and data clustering, and problems of overfitting and underfitting data.' CMOS Bulletin