Book by Alpaydin Ethem
"Sinopsis" puede pertenecer a otra edición de este libro.
"A few years ago, I used the first edition of this book as a reference book for a project I was working on. The clarity of the writing, as well as the excellent structure and scope, impressed me. I am more than pleased to find that this second edition continues to be highly informative and comprehensive, as well as easy to read and follow." Radu State Computing ReviewsReseña del editor:
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. The second edition of Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.
"Sobre este título" puede pertenecer a otra edición de este libro.
Descripción Cumberland, Rhode Island, U.S.A.: Mit Pr, 2010. Hardcover. Estado de conservación: New. 2nd Edition. Ship out 1-2 business day,Brand new,US edition, Free tracking number usually 2-4 biz days delivery to worldwide Same shipping fee with US, Canada,Europe country, Australia, item will ship out from either LA or Asia,kf. Nº de ref. de la librería ABE-7636576149
Descripción The MIT Press, 2009. Hardcover. Estado de conservación: New. Nº de ref. de la librería P11026201243X
Descripción The MIT Press. Hardcover. Estado de conservación: New. 026201243X New Condition. Nº de ref. de la librería NEW6.0934645
Descripción The MIT Press. Estado de conservación: New. 026201243X Orders ship same or next business day w/ free tracking. Choose Expedited shipping for fastest (2-6 business day) delivery. Satisfaction Guaranteed. Nº de ref. de la librería Z026201243XZN
Descripción Soft cover. Estado de conservación: New. Opt EXPEDITED shipping for 2 to 4 day delivery - Brand NEW - International Edition - 2ed - Harbound Cover, SAME Contents as in US edition - SHRINKwrapped BOXpacked - There is no CD or Access Code, unless specified above - Ships from various locations. Nº de ref. de la librería N40