Machine Learning: A Probabilistic Perspective Author: Kevin P. Murphy | Language: English | ISBN:
B00AF1AYTQ | Format: PDF
Machine Learning: A Probabilistic Perspective Description
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. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
- File Size: 32304 KB
- Print Length: 1104 pages
- Publisher: The MIT Press (September 7, 2012)
- Sold by: Amazon Digital Services, Inc.
- Language: English
- ASIN: B00AF1AYTQ
- Text-to-Speech: Not enabled
X-Ray:
- Lending: Enabled
- Amazon Best Sellers Rank: #130,473 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
- #39
in Books > Computers & Technology > Computer Science > Artificial Intelligence > Machine Learning
- #39
in Books > Computers & Technology > Computer Science > Artificial Intelligence > Machine Learning
(Disclaimer: I have worked with a draft of the book and been allowed to use the instructor's review copy for this review. I have bought the book from Amazon.co.uk, but apparently this Amazon.com review can't be tagged "verified purchase". I don't receive any compensation whatsoever for writing this review. I hope it will help you chose a machine learning textbook.)
Similar textbooks on statistical/probabilistic machine learning (links to book websites, not Amazon pages):
- Barber's Bayesian Reasoning and Machine Learning ("BRML", Cambridge University Press 2012)
- Koller and Friedman's Probabilistic Graphical Models ("PGM", MIT Press 2009)
- Bishop's Pattern Recognition and Machine Learning ("PRML", Springer 2006)
- MacKay's Information Theory, Inference and Learning Algorithms ("ITILA", CUP 2003)
- Hastie, Tibshirani and Friedman's Elements of Statistical Learning ("ESL", Springer 2009)
* Perspective: My perspective is that of a machine learning researcher and student, who has used these books for reference and study, but not as classroom textbooks.
* Audience/prerequisites: they are comparable among all the textbooks mentioned. BRML has lower expected commitment and specialization, PGM requires more scrupulous reading. The books differ in their topics and disciplinary approach, some more statistical (ESL), some more Bayesian (PRML, ITILA), some focused on graphical models (PGM, BRML). K Murphy compares MLAPP to others here. For detailed coverage comparison, read the table of contents on the book websites.
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