{"schema_version":"bf_book_companion_v1","release_id":"20261010T034321Z-reviewed-full-unique-works","book":{"amazon_purchase_url":"https://www.amazon.com/dp/0262018020?tag=bookfrontie00-20","asin":"0262018020","authors":["Kevin P. Murphy"],"book_id":"9780262018029","canonical_url":"https://bookfrontier.com/books/machine-learning-9780262018029","cover_image_url":"https://img.bookfrontier.com/9780262018029.jpg?v=b6b983223b95b74e","description":"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 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.","forthcoming":false,"isbn13":"9780262018029","publication_date":"20120824","publisher":"MIT Press","slug":"machine-learning","subtitle":"A Probabilistic Perspective","title":"Machine Learning"},"discovery":{"categories":{"all":[{"bisac_code":"MAT029010","confidence_band":"medium","label":"Bayesian Analysis","score":0.7873,"source":"bisac_hint_model"},{"bisac_code":"COM051300","confidence_band":"high","label":"Algorithms","score":0.7863,"source":"bisac_hint_model"},{"bisac_code":"COM051360","confidence_band":"medium","label":"Python Programming","score":0.748,"source":"bisac_hint_model"}],"hint":[{"bisac_code":"MAT029010","confidence_band":"medium","label":"Bayesian Analysis","score":0.7873,"source":"bisac_hint_model"},{"bisac_code":"COM051300","confidence_band":"high","label":"Algorithms","score":0.7863,"source":"bisac_hint_model"},{"bisac_code":"COM051360","confidence_band":"medium","label":"Python Programming","score":0.748,"source":"bisac_hint_model"}],"publisher":[{"bisac_code":"COM016000","confidence_band":"high","label":"Computer Vision","score":1,"source":"onix_publisher_code"},{"bisac_code":"COM094000","confidence_band":"high","label":"Machine Learning","score":1,"source":"onix_publisher_code"},{"bisac_code":"MAT002050","confidence_band":"high","label":"Linear Algebra","score":1,"source":"onix_publisher_code"}]},"genres":[{"label":"Bayesian Analysis","score":0.7873,"slug":"bayesian-analysis"},{"label":"Algorithms","score":0.7863,"slug":"algorithms"},{"label":"Python Programming","score":0.748,"slug":"python-programming"}],"reader_fit":["For upper-level undergraduates with introductory college math and beginning graduate students"],"related_lists":[],"series":null,"similar_books":[{"authors":["Sebastian Thrun","Wolfram Burgard","Dieter Fox"],"book_id":"9780262201629","cover_image_url":"https://img.bookfrontier.com/9780262201629.jpg?v=6dbfbe4456748ed3","score":0.7801,"title":"Probabilistic Robotics","url":"https://bookfrontier.com/books/9780262201629"},{"authors":["Andreas C. Müller","Sarah Guido"],"book_id":"9781449369415","cover_image_url":"https://img.bookfrontier.com/9781449369415.jpg?v=393e4ba51296a10e","score":0.7719,"title":"Introduction to Machine Learning With Python","url":"https://bookfrontier.com/books/9781449369415"},{"authors":["Mark Fenner"],"book_id":"9780134845623","cover_image_url":"https://img.bookfrontier.com/9780134845623.jpg?v=9a426a9686283467","score":0.7538,"title":"Machine Learning With Python for Everyone","url":"https://bookfrontier.com/books/9780134845623"},{"authors":["Anil Ananthaswamy"],"book_id":"9780593185766","cover_image_url":"https://img.bookfrontier.com/9780593185766.jpg?v=7e4bcc4158d9f903","score":0.7427,"title":"Why Machines Learn","url":"https://bookfrontier.com/books/9780593185766"},{"authors":["Mykel J. Kochenderfer","Tim A. Wheeler","Kyle H. Wray"],"book_id":"9780262047012","cover_image_url":"https://img.bookfrontier.com/9780262047012.jpg?v=4f6476e26303c91e","score":0.7308,"title":"Algorithms for Decision Making","url":"https://bookfrontier.com/books/9780262047012"},{"authors":["Aurélien Géron"],"book_id":"9781098125974","cover_image_url":"https://img.bookfrontier.com/9781098125974.jpg?v=08030a1e0e269d32","score":0.7305,"title":"Hands-on Machine Learning With Scikit-learn, Keras, and Tensorflow","url":"https://bookfrontier.com/books/9781098125974"},{"authors":["Chip Huyen"],"book_id":"9781098107963","cover_image_url":"https://img.bookfrontier.com/9781098107963.jpg?v=2407cd9a95157c9e","score":0.7298,"title":"Designing Machine Learning Systems","url":"https://bookfrontier.com/books/9781098107963"},{"authors":["Christian Dallago","Peter Koo","Kevin Yang","Ananthan Nambiar"],"book_id":"9781621824800","cover_image_url":"https://img.bookfrontier.com/9781621824800.jpg?v=2e169d269beab7ca","score":0.7297,"title":"Machine Learning for Protein Science and Engineering","url":"https://bookfrontier.com/books/9781621824800"}],"topics":[{"evidence_refs":["keyword:computer science"],"label":"Science","score":0.1776,"slug":"science"},{"evidence_refs":["keyword:probabilistic machine learning"],"label":"Learning","score":0.0532,"slug":"learning"},{"evidence_refs":["keyword:business"],"label":"Business","score":0.0401,"slug":"business"},{"evidence_refs":["keyword:philosophy"],"label":"Philosophy","score":0.0334,"slug":"philosophy"},{"evidence_refs":["keyword:math book"],"label":"Math","score":0.0244,"slug":"math"}],"what_you_get":["Themes: Machine learning, Probabilistic modeling, Automated data analysis.","Themes: Science, Learning, Business.","Reading lane: Bayesian Analysis and Algorithms."]},"llm_signals":{"confidence":0.97,"pacing":"unknown","reader_fit":["For upper-level undergraduates with introductory college math and beginning graduate students"],"themes":["Machine learning","Probabilistic modeling","Automated data analysis"],"tropes":[]},"source_presence":{"amazon":true,"book_detail":true,"goodreads":true,"llm_signals":true,"similarity_seed":true},"stats":{"amazon_ratings_count":0,"goodreads_ratings_count":522,"goodreads_reviews_count":18,"quality_score":0.8791}}