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Remedying this deficiency, this edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.
New to the Second Edition - Revision of the support vector machine material, including a simple implementation for experiments
- New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron
- Improved code, including better use of naming conventions in Python
- Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code.
- Each chapter includes detailed examples along with further reading and problems.
- All of the code used to create the examples is available on the author’s website.
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