d148: Book: A Course in Machine Learning

A Course in Machine Learning is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.). It’s focus is on broad applications with a rigorous backbone. A subset can be used for an undergraduate course; a graduate course could probably cover the entire material and then some.

http://ciml.info/ by Hal Daumé III

Full book: http://ciml.info/dl/v0_9/ciml-v0_9-all.pdf (PDF)

Individual Chapters (PDFs):

  1. Front Matter
  2. Decision Trees
  3. Geometry and Nearest Neighbors
  4. The Perceptron
  5. Machine Learning in Practice
  6. Beyond Binary Classification
  7. Linear Models
  8. Probabilistic Modeling
  9. Neural Networks
  10. Kernel Methods
  11. Learning Theory
  12. Ensemble Methods
  13. Efficient Learning
  14. Unsupervised Learning
  15. Expectation Maximization
  16. Semi-Supervised Learning
  17. Graphical Models
  18. Online Learning
  19. Structured Learning
  20. Bayesian Learning
  21. Back Matter


Other publications byHal Daumé III: http://www.umiacs.umd.edu/~hal/publications.html

Natural Language Processing blog: http://nlpers.blogspot.com/

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