A Course in Machine Learning

A Course in Machine Learning

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 wide variety of application fields, from medicine to advertising, from military to pedestrian. Any area where you need to make sense of data is a potential consumer of machine learning.

CIML is a set of introductory materials covering most of the main aspects of modern machine learning (supervised learning, unsupervised learning, wide-ranging methods, probabilistic models, learning theory, etc.). Its focus is on broad applications with a rigorous backbone. A subset can be used for a degree program; an undergraduate program could probably cover all the material and then some.

You can get the written material by purchasing a printed copy ($ 55), downloading the entire book, or downloading the individual chapters below. If you find the electronic version of the book useful and would like to donate a small amount to support further development, we will always be grateful! You can get the source code for the book, labs, and other educational materials on GitHub. The current version is 0.99 (the preliminary “beta” version). [You can watch v0.9 if you prefer.

Table of Contents

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

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