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.
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