Probabilistic Machine Learning by Murphy Kevin P (50 items)

MIT Press, 2012. Hardcover. Acceptable. Disclaimer:A readable copy. All pages are intact, and the cover is intact. Pages can include considerable notes-in pen or highlighter-but the notes cannot obscure the text. At ThriftBooks, our motto is: Read More, Spend Less.Dust jacket quality is not guaranteed.

The MIT Press, 8/24/2012 12:00:01 A. hardcover. Acceptable. 1.8504 in x 9.2520 in x 8.2677 in. DAMAGED - Bruise/tear to cover/spine.

The MIT Press, 8/24/2012 12:00:01 A. hardcover. Acceptable. 1.8504 in x 9.2520 in x 8.2677 in. DAMAGED - Binding is broken and loose.

The MIT Press. hardcover. Good. 9.5X6.5X1.5. Buy with confidence. Excellent Customer Service & Return policy.

MIT Press, 2012-08-24. Illustrated. hardcover. Used: Good. 8.27x1.77x9.25. Buy with confidence. Excellent Customer Service & Return policy.

Used. Textbook Only, NO ACCESS CODE, NO-CD, All orders ship SAME or NEXT business day. Expedited shipments will be delivered in 5-7 days. We are not ship to PO Box Address.

MIT Press, 2012. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In fair condition, suitable as a study copy. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,2050grams, ISBN:9780262018029

The MIT Press, 3/1/2022 12:00:01 AM. hardcover. Very Good. 1.2598 in x 9.3307 in x 8.2677 in. Very Good Condition - May show some limited signs of wear and may have a remainder mark. Pages and dust cover are intact and not marred by notes or highlighting.

The MIT Press, 3/1/2022 12:00:01 AM. hardcover. Good. 1.2598 in x 9.3307 in x 8.2677 in. Bruise/tear to cover.

Brand New,Printed in English Language, we do not ship APO, PO Box Address. Fast Delivery US Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses.

The MIT Press, 8/15/2023 12:00:01 A. hardcover. Good. 2.2835 in x 9.8031 in x 8.4646 in. Crease/tear to cover.

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.
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… Read more

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.
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… Read more

The MIT Press, 8/15/2023 12:00:01 A. hardcover. Very Good. 2.2835 in x 9.8031 in x 8.4646 in. Very Good Condition - May show some limited signs of wear and may have a remainder mark. Pages and dust cover are intact and not marred by notes or highlighting.

The MIT Press, 8/15/2023 12:00:01 A. hardcover. Good. 2.2835 in x 9.8031 in x 8.4646 in. Bruise/tear to cover.

The MIT Press, 8/15/2023 12:00:01 A. hardcover. Acceptable. 2.2835 in x 9.8031 in x 8.4646 in. DAMAGED - Bruise/tear to cover/dust jacket.

The MIT Press, 8/15/2023 12:00:01 A. hardcover. Very Good. 2.2835 in x 9.8031 in x 8.4646 in. Very Good Condition - May show some limited signs of wear and may have a remainder mark. Pages and dust cover are intact and not marred by notes or highlighting.

The MIT Press, 2012-08-24. Hardcover. Good. Textbook, May Have Highlights, Notes and/or Underlining, BOOK ONLY-NO ACCESS CODE, NO CD, Ships with Tracking

The MIT Press, 8/15/2023 12:00:01 A. hardcover. Like New. 2.2835 in x 9.8031 in x 8.4646 in. LIKE NEW!!! Has a red or black remainder mark on bottom/exterior edge of pages.

Hardcover. Good. Condition: Good; ISBN: 9780262046824; . Once purchased, we honor returns due to quality issues but won't accept reasons like 'changed my mind', 'find better price', 'order by mistake', etc