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Machine Learning: A Probabilistic Perspective
(Adaptive Computation and Machine Learning)
by
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 te ...more
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 te ...more
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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 ...more
That being said, this is perhaps the best modern "reference" text on machine learning methods. If you are a ...more
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. ...more
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. ...more
Excellent manual on statistical learning providing a simple Bayesian explanation for the most common statistical models. Some good examples: the author explains the difference between least squares, ridge, lasso, etc. from different associations of distributions for the likelihood function and prior; or the MLE (high variance/possible overfitting) is the MAP estimate (high bias) with uniform prior, etc etc. Makes something that often looks like different cooking recipes into an ontology of clear
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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.
Still relevant, still a useful reference, even in this the day of machine learning mania. Clear and well exposited.
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 ...more
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 ...more
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).
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 can become a very good reference book for machine learning. A good complementary to Pattern Recognition and Machine Learning by Bishop.
The best book on machine learning I've read, especially for those of us who like and understand the Bayesian approach to probability. It's quite math heavy and code light, but there's plenty of code available; check out the new Python code for the next edition (which itself will probably be even better than this edition, I would think).
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Oxford dictionary for machine learning. Clear formulas.
The only downside is that it lacks material for deep learning techniques.
The only downside is that it lacks material for deep learning techniques.
Either a statistics perspective or a optimization perspective has its own limitations. Maybe an approach like SGVB could be a promising option.
Solid manual for the ML field. Though I’ve found the writing heavy and it was not easy to get a grasp on the concepts, that you would have to dig in for pages.
I read until 3.5 (P82, Naive Bayes classifiers), and find it too hard and abstract to continue. Em... Maybe I should start from an easier one?
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.
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