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.
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
Source: A Course in Machine Learning