Free Introduction To Machine Learning (Adaptive Computation And Machine Learning Series) Ebooks Online

                
              
Free 
Introduction To Machine Learning (Adaptive Computation And Machine Learning Series) Ebooks Online
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. 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. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.
File Size: 15972 KB
Print Length: 640 pages
Publisher: The MIT Press; 3 edition (August 22, 2014)
Publication Date: August 22, 2014
Sold by:  Digital Services LLC
Language: English
ASIN: B00NLVNGLA
Text-to-Speech:  Not enabled
X-Ray:     Not Enabled
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Enhanced Typesetting:  Not Enabled
Best Sellers Rank: #534,839 Paid in Kindle Store (See Top 100 Paid in Kindle Store)   #120 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Machine Theory   #556 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Intelligence & Semantics   #8786 in Kindle Store > Kindle eBooks > Computers & Technology
I understand ML very well, and I find this text nearly impossible to penetrate. Formulas are reduced to their most rudimentary forms. Sure it is impressive that the author obviously has a good grasp on the topic, but there are virtually no explanations behind the math. This book was written just to show off, not to teach. Definitely the most pompous book on ML I've ever seen.
Very nice and clear content. Thanks....
Item as described. Fast shipment.
good for beginners
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