A Course in Machine Learning is an introductory book that covers most major aspects of modern machine learning.
Topics covered include: 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 and bayesian learning.
Hal Daumé III is an Assistant Professor in Computer Science at the University of Maryland.
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File size: 2.84 MB
Number of pages: 189
Download / View Link(s): A Course in Machine Learning [PDF]