Machine learning: a probabilistic perspective
Author:
Publisher:
MIT Press
Pub. Date:
[2012]
Language:
English
Description
"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"--Back cover.
More Details
ISBN:
9780262018029
9780262305242
9780262305242
More Copies In Prospector
Loading Prospector Copies...
Staff View
Grouping Information
Grouped Work ID | 77f9963c-5067-b543-b6d9-d5dcd5bf63b1 |
---|---|
Grouping Title | machine learning a probabilistic perspective |
Grouping Author | kevin p murphy |
Grouping Category | book |
Grouping Language | English (eng) |
Last Grouping Update | 2024-04-07 17:29:10PM |
Last Indexed | 2024-04-25 23:36:07PM |
Solr Fields
accelerated_reader_point_value
0
accelerated_reader_reading_level
0
author
Murphy, Kevin P., 1970-
author_display
Murphy, Kevin P.
available_at_adams
Adams State University
collection_adams
Main Collection
detailed_location_adams
ASU Main (3rd floor)
display_description
"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"--Back cover.
format_adams
Book
format_category_adams
Books
id
77f9963c-5067-b543-b6d9-d5dcd5bf63b1
isbn
9780262018029
9780262305242
9780262305242
itype_adams
Book
last_indexed
2024-04-26T05:36:07.857Z
lexile_score
-1
literary_form
Non Fiction
literary_form_full
Non Fiction
local_callnumber_adams
Q325.5 .M87 2012
owning_library_adams
Adams State University
owning_location_adams
Adams State University
primary_isbn
9780262018029
publishDate
2012
publisher
MIT Press
recordtype
grouped_work
series
Adaptive Computation and Machine Learning Ser
series_with_volume
Adaptive Computation and Machine Learning Ser|
subject_facet
Electronic books
Machine learning
Probabilities
Machine learning
Probabilities
title_display
Machine learning : a probabilistic perspective
title_full
Machine Learning : A Probabilistic Perspective Murphy, Kevin P.
Machine learning : a probabilistic perspective / Kevin P. Murphy
Machine learning : a probabilistic perspective / Kevin P. Murphy
title_short
Machine learning
title_sub
a probabilistic perspective
topic_facet
Machine learning
Probabilities
Probabilities
Solr Details Tables
item_details
Bib Id | Item Id | Shelf Loc | Call Num | Format | Format Category | Num Copies | Is Order Item | Is eContent | eContent Source | eContent URL | Detailed Status | Last Checkin | Location |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
proquestebookwestern:EBC3339490 | EBC3339490 | ProQuest Ebook Central (Western) | Online ProQuest Ebook Central (Western) | eBook | eBook | 1 | false | true | ProQuest Ebook Central (Western) | https://ebookcentral.proquest.com/lib/wscc-ebooks/detail.action?docID=3339490 | Available Online | ProQuest Ebook Central (Western) | |
ils:.b41566610 | .i82427240 | ASU Main (3rd floor) | Q325.5 .M87 2012 | 1 | false | false | On Shelf | Jan 05, 2024 | as |
record_details
Bib Id | Format | Format Category | Edition | Language | Publisher | Publication Date | Physical Description | Abridged |
---|---|---|---|---|---|---|---|---|
proquestebookwestern:EBC3339490 | eBook | eBook | English | MIT Press | 2012 | 1 online resource (1098 pages) | ||
ils:.b41566610 | Book | Books | English | MIT Press | [2012] | xxix, 1067 pages : illustrations (some color) ; 24 cm. |
scoping_details_adams
Bib Id | Item Id | Grouped Status | Status | Locally Owned | Available | Holdable | Bookable | In Library Use Only | Library Owned | Holdable PTypes | Bookable PTypes | Local Url |
---|---|---|---|---|---|---|---|---|---|---|---|---|
ils:.b41566610 | .i82427240 | On Shelf | On Shelf | false | true | true | false | false | true | 56, 57, 58, 59 |