File Name: adaptive computation and machine learning series .zip
- Adaptive computation and machine learning
- Deep learning: adaptive computation and machine learning
- Adaptive Computation and Machine Learning series- Deep learning-The MIT Press (2016).pdf
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Adaptive computation and machine learning
The goal of building systems that can adapt to their environments and learn from their experience has attracted researchers from many fields, including computer science, engineering, mathematics, physics, neuroscience, and cognitive science. Out of this research has come a wide variety of learning techniques, including methods for learning decision trees, decision rules, neural networks, statistical classifiers, and probabilistic graphical models. The researchers in these various areas have also produced several different theoretical frameworks for understanding these methods, such as computational learning theory, Bayesian learning theory, classical statistical theory, minimum description length theory, and statistical mechanics approaches. These theories provide insight into experimental results and help to guide the development of improved learning algorithms. A goal of the series is to promote the unification of the many diverse strands of machine learning research and to foster high quality research and innovative applications. This series will publish works of the highest quality that advance the understanding and practical application of machine learning and adaptive computation.
Introduction to Machine Learning Adaptive Computation and Machine Learning series details Details Product: 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, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. 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. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book 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.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Murphy Published in Adaptive computation and…. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data.
Deep learning: adaptive computation and machine learning
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Rasmussen and C. Rasmussen , C. Williams Published Computer Science.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Dietterich Published Computer Science. All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means including photocopying, recording, or information storage and retrieval without permission in writing from this is the last candidate. Overview Dataset shift is a challenging situation where the joint distribution of inputs and outputs differs between the training and test stages.
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Adaptive Computation and Machine Learning series- Deep learning-The MIT Press (2016).pdf
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