Applied Machine Learning

Research output: Book/Report/Conference proceedingBook


Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren't necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one's own code. A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use).

Original languageEnglish (US)
Number of pages494
ISBN (Electronic)9783030181147
ISBN (Print)9783030181130
StatePublished - Jan 1 2019


  • EM
  • Markov chains
  • OCA
  • PSCS
  • SVM
  • generalized linear models
  • linear regression
  • machine learning
  • model selection
  • naive bayes
  • nearest neighbor
  • structure learning

ASJC Scopus subject areas

  • General Computer Science


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