*The following content is totally copied from the website of A Course in 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.

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This book is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it or re-use it under the terms of the CIML License online at ciml.info/LICENSE. You may not redistribute it yourself, but are encouraged to provide a link to the CIML web page for others to download for free. You may not charge a fee for printed versions, though you can print it for your own use.

Individual Chapters:

- 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