best book for probability and statistics for machine learning

Original URL: http://m.blog.csdn.net/article/details?id=49130173 first, transcendental probability, posterior probability, Bayesian formula, likelihood function In machine learning, these concepts are always involved, but never really understand the connection between them. Here's a good idea to start with the basics

some examples of beta functions:It is of the following nature:Pareto DistributionThe Pareto principle must have heard it, is the famous long tail theory, Pareto distribution expression is as follows:Here are some examples: the left image shows the Pareto distribution under different parameter configurations.Some of the properties are as follows:ReferenceprmlmlapCopyright NOTICE: This article for Bo Master original article, without Bo Master permission not reproduced. cs281:advanced

) = P (A, B)/P (B), which can be P (, b) = P (A | B) * P (B ). the Bayesian formula is introduced in this way. A general idea of this article: First, let's talk about a basic Bayesian learning framework that I have summarized, and then give a few simple examples to illustrate these frameworks, finally, I would like to give a more complex example, which is explained by the modules in the Bayesian machine

: Variance: Variance can be used to estimate the intensity of change of a function f near his expectation. It is defined If the variable X itself is considered, the variance of X is also available: Note: (skipped in the book) This equation is actually derived from the definition of variance: In addition, we define two random variables.Covariance: X, YDegree of change together, if XAnd yIndependent of each other, the covaria

estimate the intensity of change of a function f near his expectation. It is defined If the variable X itself is considered, the variance of X is also available: Note: (skipped in the book) This equation is actually derived from the definition of variance: In addition, we define two random variables.Covariance: It indicates the degree to which x and y change together. If X and Y are independent of each other, the covariance is 0.

Sciences, by Avrim Blum, John Hopcroft, and Ravindran Kannan," one of the authors of John Hopcroft is a Turing Award winner. In the frontier of this book, it is mentioned that the development of computer science can be divided into three stages: early, middle and present. The early days were for computers to work, focusing on developing programming languages, compiling principles, operating systems, and studying mathematical theories that supported t

Encounter a new problem So far, at least a few issues need to be addressed: What to do if the analytic solution cannot be solved. The answer is: Iterative Search solver (optimization problem) If the number of samples is particularly large, how to deal with it. The answer is: recursive solver (online learning optimization problem) What if the error is not a normal distribution? The answer is: generalized linear regression If the dimension of this

foundation of mathematics, so that students can not be brought into the right path. At least as a class student, I feel that way. The result is a sense that the course is independent of one area and is very isolated. From some foreign books can be seen, machine learning is actually a multi-disciplinary derivative, and a lot of engineering field theory has a close connection, so that at least let us this be

scope of this model, such as medical diagnosis and most machine learning. However, it also has some controversy. When it comes to this, it will go back to the topic of debate between the Bayesian School and the frequency School for several hundred years, because the Bayesian school assumes some prior probabilities, in contrast, the frequency school thinks that this anterior is somewhat subjective, and the

obtained for all possible combinations x,u. Complete data is the complete probability, and incomplete data is the probability of its marginal missing variable. In M-step, the system parameter theta is updated with sufficient statistics.For example, in the Bayesian classifier, we only have data and no class value for the data. (It really can be lost .....) At this point, if the EM algorithm is used, the Bay

Today, Google's robot Alphago won the second game against Li Shishi, and I also entered the stage of the probability map model learning module. Machine learning fascinating and daunting.--Preface1. Learning based on PGMThe topological structure of Ann Networks is often simil

I. Introduction Recently I have written many learning notes about machine learning, which often involves the knowledge of probability theory. Here I will summarize and review all the knowledge about probability theory for your convenience and share it with many bloggers, I h

provides an in-depth explanation from a large number of unstructured web The technique of extracting and generating knowledge in data. The first chapter of the book deals with web web web indexing mechanism and keyword-based or similarity-based search mechanism, and then systematically describe the basics of web mining , focusing on hypertext-based machine le