# MACHINE LEARNING A PROBABILISTIC PERSPECTIVE KEVIN MURPHY PDF

Machine learning: a probabilistic perspective / Kevin P. Murphy. p. cm. — ( Adaptive computation and machine learning series). Includes Degenerate pdf. MACHINE LEARNING: A PROBABILISTIC PERSPECTIVE. Kevin P. Murphy. University of British Columbia, Canada bestthing.info˜. Kevin P. Murphy The probabilistic approach to machine learning is closely related to view of the field through the lens of probabilistic modeling and inference. statistics, available at bestthing.info~tibs/stata/ bestthing.info

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Machine Learning A Probabilistic Perspective Kevin P. Murphy “An astonishing machine learning book: intuitive, full of examples, fun to read. Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) [Kevin P. Murphy] on bestthing.info *FREE* shipping on. Machine learning: a probabilistic perspective / Kevin P. Murphy. –. Cambridge Some basic concepts in machine learning. 16 Degenerate pdf.

Similarly the correct label for the blue arrow is unclear.

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The reader is assumed to already have some familiarity with basic concepts in probability. If not please consult Chapter 2 for a refresher if necessary. We will denote the probability distribution over possible labels given the input vector x and training set D by py xD. In general this represents a vector of length C.

In our notation we make explicit that the probability is conditional on the test input x as well as the training set D by putting these terms on the right hand side of the conditioning bar.

We are also implicitly conditioning on the form of model that we use to make predictions. When choosing between diferent models we will make this assumption explicit by writing py xDMwhere M denotes the model. However if the model is clear from context we will drop M from our notation for brevity. Another application where it is important to assess risk is when playing TV game shows such as Jeopardy. In this game contestants have to solve various word puzzles and answer a variety of trivia questions but if they answer incorrectly they lose money.

Watson uses a variety of interesting techniques Ferrucci et al. We will discuss some of the basic principles behind systems such as SmartASS later in this book. Supervised learning 5 words documents 10 20 30 40 50 60 70 80 90 Figure 1. We only show rows for clarity.

Each row is a document represented as a bag-of-words bit vector each column is a word. The red lines separate the 4 classes which are in descending order comp rec sci talk these are the titles of USENET groups. We can see that there are subsets of words whose presence or absence is indicative of the class.

Figure generated by newsgroupsVisualize. We have already mentioned some important applciations. We give a few more examples below. A common way to represent variable-length documents in feature-vector format is to use a bag of words representation. This is explained in detail in Section 3.

In Exercise 8. However when we look at the brain we seem many levels of processing.

It is believed that each level is learning features or representations at increasing levels of abstraction. For example the standard model of the visual cortex Hubel and Wiesel Serre et al. Palmer Kandel et al. This observation has inspired a recent trend in machine learning known as deep learning see e.

Bengio deeplearning. Note the idea can be applied to non-vision problems as well such as speech and language. However we caution the reader that the topic of deep learning is currently evolving very quickly so the material in this chapter may soon be outdated. Acquiring enough labeled data to train such models is difcult despite crowd sourcing sites such as Mechanical Turk.

Figure The most natural way to perform this is to use generative models. In this section we discuss three diferent kinds of deep generative models: directed undirected and mixed. There have been some attempts to use computer graphics and video games to generate realistic-looking images of complex scenes and then to use this as training data for computer vision systems.

Deeplearning a b c Figure Observed variables are at the bottom. The bottom level contains the observed pixels or whatever the data is and the remaining layers are hidden. We have assumed just 3 layers for notational simplicity. The number and size of layers is usually chosen by hand although one can also use non-parametric Bayesian methods Adams et al.

We shall call models of this form deep directed networks or DDNs. If all the nodes are binary and all CPDs are logistic functions this is called a sigmoid belief net Neal Slow inference also results in slow learning. For example we can stack a series of RBMs on top of each other as shown in Figure Deepgenerativemodels where we are ignoring constant ofset or bias terms.

The main disadvantage is that training undirected models is more difcult because of the partition function. However below we will see a greedy layer-wise strategy for learning deep undirected models. In particular suppose we construct a layered model which has directed arrows except at the top where there is an undirected bipartite graph as shown in Figure This model is known as a deep belief network Hinton et al.

The advantage of this peculiar architecture is that we can infer the hidden states in a fast bottom-up fashion. Ian Goodfellow. The Elements of Statistical Learning: Reinforcement Learning: Probabilistic Graphical Models: Daphne Koller. Learning From Data. Yaser S. Review This comprehensive book should be of great interest to learners and practitioners in the field of machine learning. Read more.

## 35 Free Online Books on Machine Learning

Product details Series: Adaptive Computation and Machine Learning series Hardcover: English ISBN Try the site edition and experience these great reading features: Share your thoughts with other customers.

Write a customer review. Read reviews that mention machine learning great book pattern recognition reference book bayesian reasoning elements of statistical good reference statistical learning feel like recognition and machine bought this book good job well organized deep learning book covers think this book learning book bishop book book if you already learning textbook.

Top Reviews Most recent Top Reviews. There was a problem filtering reviews right now. Please try again later. Hardcover Verified download. In a nutshell, the value of reading Murphy's Machine Learning highly depends on what you expect to get out of it.

Future chapters are constantly referenced in the text as if you have already read them! Perplexingly, meaningful explanations of concepts are often delayed by multiple chapters. BIC is introduced in Ch. I particularly enjoyed the example of calculating the posterior probability of user ratings of two different items on site. In summary, if you are an instructor that wants their students to learn how to read challenging exposition to prepare them for reading research papers in the field or if you wish to use this as a reference, then this is a good choice.

Otherwise, pass. I have worked with a draft of the book and been allowed to use the instructor's review copy for this review. I have bought the book from site. I don't receive any compensation whatsoever for writing this review. I hope it will help you chose a machine learning textbook. My perspective is that of a machine learning researcher and student, who has used these books for reference and study, but not as classroom textbooks.

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For detailed coverage comparison, read the table of contents on the book websites. MLAPP stands out for covering more advanced and current research topics: The book is "open" and vivid, doesn't shy away from current research and advanced concepts. This seems to be purposeful, as it shows in many aspects: Whereas other books will produce their own pictures and diagrams themselves eg, PRML has a distinctive clarity and style in its illustrations , MLAPP takes many of its colour illustrations from other people's publications; therefore it can select the most pithy and relevant pictures to make a point.

You could think that reproductions may be illegible and require extra effort to interpret because they come from a variety of sources; I have found that the bonus coming from having precisely the right image prevails. All this connects the material with research and new ideas in a fine way -- which other textbooks don't achieve, I find. For instance, PGM defers references to a literature section at the end of each chapter, resulting in a more self-contained, but more poorly "linked" text.

Another distinctive feature is that the author clearly has tried to include didactic aids gathered over the years, such as recaps, comparative tables, diagrams, much in the spirit of the "generative model of generative models" Roweis and Ghahramani: Other editorial features worth mentioning are - compared to others, helpful mentions of terminology, e.

PGM stands out as excruciatingly precise on this aspect. Layout rather plain and homogeneous, much like PRML. Also, code may clarify an algorithm, even when presented in pseudo-code. This will no doubt reduce its diffusion.

My own take on the underlying controversy is in favor of distributing the PDF: C such as male or female or that y i is a real-valued scalar such as income level. Another variant known as ordinal regression occurs where label space Y has some natural ordering such as grades A—F.

The second main type of machine learning is the descriptive or unsupervised learning approach. This is sometimes called knowledge discovery.

## Machine Learning A Probabilistic Approach

There is a third type of machine learning known as reinforcement learning which is somewhat less commonly used. This is useful for learning how to act or behave when given occasional reward or punishment signals. For example consider how a baby learns to walk. Unfortunately RL is beyond the scope of this book although we do discuss decision theory in Section 5. See e. Kaelbling et al. Supervised learning 3 a b Figure 1.

Some labeled training examples of colored shapes along with 3 unlabeled test cases. Row i represents the feature vector xi. C with C being the number of classes. If the class labels are not mutually exclusive e. One way to formalize the problem is as function approximation.

We use the hat symbol to denote an estimate. Our main goal is to make predictions on novel inputs meaning ones that we have not seen before this is called generalization since predicting the response on the training set is easy we can just look up the answer. We have two classes of object which correspond to labels 0 and 1.

The inputs are colored shapes. The input features x can be discrete continuous or a combination of the two.

In addition to the inputs we have a vector of training labels y. In Figure 1. None of these have been seen before. Thus we are required to generalize beyond the training set. A slide 9: Introduction reasonable guess is that blue crescent should be y1 since all blue shapes are labeled 1 in the training set. The yellow circle is harder to classify since some yellow things are labeled y1 and some are labeled y0 and some circles are labeled y1 and some y0.

Consequently it is not clear what the right label should be in the case of the yellow circle. Similarly the correct label for the blue arrow is unclear. The reader is assumed to already have some familiarity with basic concepts in probability. If not please consult Chapter 2 for a refresher if necessary.

We will denote the probability distribution over possible labels given the input vector x and training set D by py xD. In general this represents a vector of length C. In our notation we make explicit that the probability is conditional on the test input x as well as the training set D by putting these terms on the right hand side of the conditioning bar. We are also implicitly conditioning on the form of model that we use to make predictions.

When choosing between diferent models we will make this assumption explicit by writing py xDMwhere M denotes the model. However if the model is clear from context we will drop M from our notation for brevity.

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Another application where it is important to assess risk is when playing TV game shows such as Jeopardy. In this game contestants have to solve various word puzzles and answer a variety of trivia questions but if they answer incorrectly they lose money.

Watson uses a variety of interesting techniques Ferrucci et al. We will discuss some of the basic principles behind systems such as SmartASS later in this book. Supervised learning 5 words documents 10 20 30 40 50 60 70 80 90 Figure 1. We only show rows for clarity.

Each row is a document represented as a bag-of-words bit vector each column is a word. The red lines separate the 4 classes which are in descending order comp rec sci talk these are the titles of USENET groups. We can see that there are subsets of words whose presence or absence is indicative of the class. The data is available from http: Figure generated by newsgroupsVisualize.

We have already mentioned some important applciations. We give a few more examples below. A common way to represent variable-length documents in feature-vector format is to use a bag of words representation.

This is explained in detail in Section 3. In Exercise 8. However when we look at the brain we seem many levels of processing. It is believed that each level is learning features or representations at increasing levels of abstraction.

For example the standard model of the visual cortex Hubel and Wiesel Serre et al. Palmer Kandel et al. This observation has inspired a recent trend in machine learning known as deep learning see e. Bengio deeplearning.

Note the idea can be applied to non-vision problems as well such as speech and language. However we caution the reader that the topic of deep learning is currently evolving very quickly so the material in this chapter may soon be outdated. Acquiring enough labeled data to train such models is difcult despite crowd sourcing sites such as Mechanical Turk.In this section, we discuss three different kinds of deep generative models: Watson uses a variety of interesting techniques Ferrucci et al.

Thus, its readers will become articulate in a holistic view of the state-of-the-art and poised to build the next generation of machine learning algorithms. Yaser S. Authors Kevin P. It is believed that each level is learning features or representations at increasing levels of abstraction. English Choose a language for shopping. Oct 24, - This approach of 'learning' a BN based on data—such as that discussed by Heckerman, Geiger, and Chickering in their machine learning paper—is useful when relevant data are available.

We will often use the language of graphical models to specify our models in a concise and intuitive way.

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Conversely, any given algorithm can often be applied to a variety of models.

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