James Rehg
Georgia Institute of Technology
Book Recommendations:
Recommended by James Rehg
“Dr. Wu has written a valuable book that could not be more timely: the commoditization of machine learning is putting increasingly powerful tools for working with data in the hands of an increasingly broad population of users and practitioners. However, using these tools correctly and interpreting their outputs properly still require significant expertise. This book fills the gap between the classic pattern-recognition texts that assume a substantial amount of background knowledge and preparation and the innumerable internet blog posts which are highly accessible but often superficial. I am sure this self-contained and useful book will enjoy widespread adoption, and I recommend it highly.” (from Amazon)
This textbook introduces fundamental concepts, major models, and popular applications of pattern recognition for a one-semester undergraduate course. To ensure student understanding, the text focuses on a relatively small number of core concepts with an abundance of illustrations and examples. Concepts are reinforced with hands-on exercises to nurture the student's skill in problem solving. New concepts and algorithms are framed by real-world context and established as part of the big picture introduced in an early chapter. A problem-solving strategy is employed in several chapters to equip students with an approach for new problems in pattern recognition. This text also points out common errors that a new player in pattern recognition may encounter, and fosters the ability for readers to find useful resources and independently solve a new pattern recognition task through various working examples. Students with an undergraduate understanding of mathematical analysis, linear algebra, and probability will be well prepared to master the concepts and mathematical analysis presented here.