A Course in Machine Learning

Written by: Hal Daumé III

Hal Daumé III is an Associate Professor in Computer Science at the University of Maryland. His specialty is “in developing new learning algorithms for prototypical problems that arise in the context of language processing and artificial intelligence.” He worked with Eric Brill in 2003 in the machine learning and applied statistics group at Microsoft Research.

A Course in Machine Learning was written to provide an introduction to the field of machine learning. Daumé structured the book to make it a learning tool that focuses more on ideas and models and less on math. The last time I visited his site, the latest updates were made September 2014.

Students should have some background in differential calculus and discrete math. Programming skills are recommended as is “a little bit of linear algebra.” (If you need a refresher in any of the pre-requisites, you can check out our sections on those topics for online textbooks that can help.)

Chapter Titles for A Course in Machine Learning

  • Front Matter
  • Decision Trees
  • Geometry and Nearest Neighbors
  • The Perceptron
  • Machine Learning in Practice
  • Beyond Binary Classification
  • Linear Models
  • Probabilistic Modeling
  • Neural Networks
  • Kernel Methods
  • Learning Theory
  • Ensemble Methods
  • Efficient Learning
  • Unsupervised Learning
  • Expectation Maximization
  • Semi-Supervised Learning
  • Graphical Models
  • Online Learning
  • Structured Learning
  • Bayesian Learning
  • Back Matter

View this Free Online Material at the source:
A Course in Machine Learning

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