statistics for machine learning udemy

------------------------------------------------------------------------------------------------------------------ This article is excerpted from the courseware "Machine Learning" by Andrew Ng of Stanford University. Why is the Department of biological engineering at Ocean University the best? They are incomparable to other schools in studying the environment and basis of marine biology.Sat2 mathematical

Original address: http://www.mimno.org/articles/ml-learn/Written by David MimnoOne of my students recently asked me for advice on learning ML. Here's what I wrote. It ' s biased toward my own experience, but should generalize.My Current Favorite Introduction is Kevin Murphy's book (Machine learning). Might also want to look at books by Chris Bishop (Pattern recog

Naive Bayesian Classification (NBC) is the most basic classification method in machine learning, and it is the basis of the comparison of classification performance of many other classification algorithms, and the other algorithms are based on NBC in evaluating performance. At the same time, for all machine learning me

Category Name Keywords Supervised Classification Decision tree Information Gain Classification regression tree Gini index, Gini 2 Statistics, pruning Naive Bayes Non-parameter estimation, Bayesian Estimation Linear Discriminant Analysis Fishre identification, feature vector Solution K nearest Similarity measurement: Euclidean distance, block distance, edit

as:If the covariance matrix is a unit matrix (the independent distribution of each sample vector), the formula becomes:That's the Euclidean distance.If the covariance matrix is a diagonal matrix, the formula becomes the normalized Euclidean distance.(2) The advantages and disadvantages of Markov distance: dimension independent, exclude the interference between the correlations between variables.(3) MATLAB calculation (1 2), (1 3), (2 2), (3 1) of the Markov distance between 22X = [1 2; 1 3; 2 2

, activating the back of the nerve layer, the final output layer of the nodes on the node on behalf of a variety of fractions, example to get the classification result of Class 1The same input is transferred to different nodes and the results are different because the respective nodes have different weights and biasThis is forward propagation.10. MarkovVideoMarkov Chains is made up of state and transitionsChestnuts, according to the phrase ' The quick brown fox jumps over the lazy dog ', to get

direction of two vectors, which is borrowed from the machine learning to measure the difference between sample vectors.(1) The angle cosine formula of vector A (x1,y1) and Vector B (x2,y2) in two-dimensional space:(2) Angle cosine of two n-dimensional sample points a (x11,x12,..., x1n) and B (x21,x22,..., x2n)Similarly, for two n-dimensional sample points a (x11,x12,..., x1n) and B (x21,x22,..., x2n), a co

(decision boundary) is equivalent to the original linear regression3.1 Parametric SolutionAfter the mathematical form of the model is determined, the rest is how to solve the parameters in the model. One of the most common methods in statistics is the maximum likelihood estimation, which is to find a set of parameters, so that the likelihood value (probability) of our data is greater under this set of parameters. In a logistic regression model, the l

index to optimize, need to consider a number of goals, it may be necessary to integrate them into an expression (such as the average number of error indicators), or to define the satisfaction index and optimization indicators.(4) machine learning is a high-speed iterative process: You may want to try a lot of ideas before the final satisfying scenario comes up.(5) Having development sets, test sets, and si

, temperature) =0.970-(2/5) 1.0-(2/5) 1.0-(1/5) 0.0=.570Gain (Ssunny, Wind) =0.970-(2/5) 1.0-(3/5). 918=.019Five A hypothetical space search in decision tree learningThe hypothetical space in the ID3 algorithm contains all decision trees, which is a complete space for finite discrete-valued functions of existing properties.When the decision tree space is changed, ID3 only maintains a single current hypothesis.The basic ID3 algorithm does not backtrack in the search.The ID3 algorithm uses all of

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, Memo. 1. Prior probability A priori probability relies only on subjective empirical esti

\%s.txt'I'R'). Read () forIinchRange (1, 20)] Transpamlist= [Open (r'C:\Users\Administrator\Desktop\machinelearninginaction\Ch04\email\spam\%s.txt'I'R'). Read () forIinchRange (1, 20)] forLineinchtranhamlist:temp. Set_tran_data (line, True) forLineinchtranspamlist:temp. Set_tran_data (line, False) testlist= [Open (r'C:\Users\Administrator\Desktop\machinelearninginaction\Ch04\email\ham\%s.txt'I'R'). Read () forIinchRange (21, 26)] forLineinchtestlist:PrintTemp.classifiy (line)The

Chisqtestresult object with P-value, Test statistics and degrees of freedom for each feature. Labels and features must be discrete. linear regression Classification and regression, supervised learning, all used to Mllib.regression.LabledPoint class, Lable+freature vectorRefers to the linear combination of features to predict output values, also supports regular regression of L1 and L2, Lasso and Ridge regr

. For example, if other conditions are certain, smokers who are at risk of lung cancer are 5 times times more likely to be non-smokers, then if I now know that a person is lung cancer, I would like to ask you whether this person smokes or smokes. How do you judge? You probably don't know anything about this person, and the only thing you've got is that smoking is more prone to lung cancer, so you're guessing this guy doesn't smoke? I believe you are more likely to say that this man smokes. Why?

MySpace qizmt is a mapreduce framework designed to run and develop distributed computing application projects running on Windows Server large-scale clusters. MySpace qizmt is an open-source framework initiated by MySpace to develop trustworthy, scalable, and super-Simple distributed application projects. Open Source Address: http://code.google.com/p/qizmt /. Infer. NET is an open-source framework that runs Bayesian inference in graphical mode. It is also used for ProbabilityProgramDesign. Open

decision tree of machine learning algorithmWhat is a decision treeDecision Trees (decision tree) are simple but widely used classifiers. By training data to build decision tree, the unknown data can be efficiently classified. The decision-making number has two advantages:1 Thedecision tree model can be read well, descriptive, and helpful for manual analysis;2) High efficiency, the decision tree only need to

ADD1 () DROP1 () 9. Regression Diagnostics Does the sample conform to the normal distribution? Normality test: function shapiro.test (X$X1) The distribution of normality Learning set/Is there outliers? How to find Outliers is the linear model reasonable? Maybe the relationship between nature is more complicated. Whether the error satisfies the independence, equal variance (the error is no

], respectively, is defined as:Visually, covariance represents the expectation of the total error of two variables.If the trend of the two variables is the same, that is, if one is greater than the expected value of the other, then the covariance between the two variables is positive, and if the two variables change in the opposite direction, that is, one of the variables is greater than its own expectation, and the other one is less than its own expectation. Then the covariance between the two

instrumental permutation test (permutation test) in the use of statistics in RF is used to measure the importance of feature items. n samples, D dimensions per sample, in order to measure the importance of one of the features di, according to permutation test the N sample of the di features are shuffled shuffle, shuffle before and after the error subtraction is the importance of this feature. RF often does not use permutation Test during trai

One of the top ten algorithms for Machine Learning: EM algorithm. One of the top 10, which makes people think Nb-rich. What is Nb? We generally say someone is Nb because he can solve problems that others cannot solve. Why God is God, because God can do things that many people cannot do. So what problems can the EM algorithm solve? Or the reason why the EM algorithm came to this world has attracted so many p