A Course in Machine Learning

by Hal Daumé III

Machine learning is the study of algorithms that learn from data and experience. It is applied in a vast variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential consumer of machine learning.

CIML 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.

Support and Mailing Lists:

If you would like to be informed when new versions of CIML materials are released, please join the CIML mailing list. If you find errors in the book, please fill out a bug report. If you’re the first to submit an error, you’ll get listed in the acknowledgments!

Individual Chapters:

Front Matter
Decision Trees
Limits of Learning
Geometry and Nearest Neighbors
The Perceptron
Practical Issues
Beyond Binary Classification
Linear Models
Bias and Fairness
Probabilistic Modeling
Neural Networks
Kernel Methods
Learning Theory
Ensemble Methods
Efficient Learning
Unsupervised Learning
Expectation Maximization
Structured Prediction
Imitation Learning
Back Matter

Acknowledgments

Thanks to everyone who was ever a teacher or student of mine, to those who provided feedback on drafts, and to colleagues for encouragement to get this done! Special thanks to: TODO…

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