Stephen Marsland

Machine Learning (eBook, PDF)

An Algorithmic Perspective, Second Edition

• Format: PDF

Produktbeschreibung

This bestseller 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. Along with improved Python code, this second edition includes two new chapters on deep belief networks and Gaussian processes. It incorporates new material on the support vector machine, random forests, the perceptron convergence theorem, filters, and more. All of the code is available on the author's website.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 457
• Erscheinungstermin: 8. Oktober 2014
• Englisch
• ISBN-13: 9781466583337
• Artikelnr.: 55339725

Autorenporträt

Stephen Marsland is a professor of scientific computing and the postgraduate director of the School of Engineering and Advanced Technology (SEAT) at Massey University. His research interests in mathematical computing include shape spaces, Euler equations, machine learning, and algorithms. He received a PhD from Manchester University

Inhaltsangabe

Introduction. Linear Discriminants. The Multi-Layer Perceptron. Radial
Basis Functions and Splines. Support Vector Machines. Learning with Trees.
Decision by Committee: Ensemble Learning. Probability and Learning.
Unsupervised Learning. Dimensionality Reduction. Optimization and Search.
Evolutionary Learning. Reinforcement Learning. Markov Chain Monte Carlo
(MCMC) Methods. Graphical Models. Python.