A First Encounter with Machine Learning
by Max Welling
Publisher: University of California Irvine 2011
Number of pages: 93
The book you see before you is meant for those starting out in the field of machine learning, who need a simple, intuitive explanation of some of the most useful algorithms that our field has to offer. A first read to wet the appetite so to speak, a prelude to the more technical and advanced text books.
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by Hal Daumé III - ciml.info
Tis is a set of introductory materials that covers most major aspects of modern machine learning (supervised and unsupervised learning, large margin methods, probabilistic modeling, etc.). It's focus is on broad applications with a rigorous backbone.
by Robert E. Schapire, Yoav Freund - The MIT Press
Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate 'rules of thumb'. A remarkably rich theory has evolved around boosting, with connections to a range of topics.
by Abdelhamid Mellouk, Abdennacer Chebira - InTech
Neural machine learning approaches, Hamiltonian neural networks, similarity discriminant analysis, machine learning methods for spoken dialogue simulation and optimization, linear subspace learning for facial expression analysis, and more.
by Mark Watson - Lulu.com
The book uses the author's libraries and the best of open source software to introduce AI (Artificial Intelligence) technologies like neural networks, genetic algorithms, expert systems, machine learning, and NLP (natural language processing).