Daumé III Hal. A Course in Machine Learning

Ciml.info; Published by TODO, 2015. — 227 p.This 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.Decision Trees
Geometry and Nearest Neighbors
The Perceptron
Practical Issues
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 Tasks
Bayesian Learning
Code and Datasets