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, a unified, probabilisToday 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, 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.

Machine Learning A Probabilistic Perspective 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 u

Hard pressed to say anyone has actually "read" this whole book--it reads like a smattering of all popular machine learning algorithms. I would not recommend it for an introduction to machine learning, not due to the technical prowess required (as it is actually much lighter on math than other similar books), but moreso due to the method and depth in which the author introduces the material.That being said, this is perhaps the best modern "reference" text on machine learning methods. If you are a [...]

Well, although this book is not made for reading purposes (in the common usage of the word reading). But I found it really interesting. It contains every single thing that is related with Machine Learning, every algorithm that is used, every modern approach that is developed. I liked how Murphy ordered the book's topics. Surely it is not recommended for everyone, but at least recommended for those who want to understand deeply Machine Learning in a very comprehensive way.

Content of the book is fantastic (five stars), albeit slightly out of date in 2016. However, the first printing is so full of typos (zero stars) that it is difficult to understand how the version ever got printed. Clearly nobody read through it before printing approval. I would not recommend the first edition to anyone unless they are experts with the ability to verify and if necessary rewrite every single equation.

This can become a very good reference book for machine learning. A good complementary to Pattern Recognition and Machine Learning by Bishop.

This book is amazing. I really enjoy reading it. Kevin Murphy is a great teacher and excellent researcher. You can get lots of insights that absent from practical books or blogs.There are many typos in the first 3 printings. The 4th (and later) is much better. What I bought (11/24/2017) is the 6th printing (the same as the 4th).

This substantial book is a deep and detailed introduction to the field of machine learning, using probabilistic methods. It is aimed at a graduate-level readership and assumes a mathematical background that includes calculus, statistics and linear algebra.The book opens with a brief survey of the kinds of problems to which machine learning can be applied, and sketches the types of methods that can be used to model these problems.After a short introduction to probability, the remaining 27 chapter [...]

Solid, but it needed better notation. The notation got very cumbersome by the end and obscured a lot of the intuition behind what was going on.