statistics and machine learning toolbox matlab

MATLAB machine learning did not see what tutorial, only a series of functions, had to record:Matlab Each machine learning method is implemented in many ways, and can be advanced configuration (such as the training decision tree when the various parameters set), here due to s

Machine Learning-Overview of common matlab programming commands -- Summary from ng-ml-class octave/MATLAB tutorial CourseraA. basic operations and moving data around1 in command line mode, you can use Shift + press enter to append the next line to output 2 length command to apply to the matrix, and return a higher one-

1. The complete course of statistics all of statistics Carnegie Kimelon Wosseman 2. Fourth edition, "Probability Theory and Mathematical Statistics" Morris. Heidegger, Morris H.degroot, and Mark. Schevish (Mark j.shervish) 3. Introduction to Linear algebra, Gilbert. Strong--Online video tutorials are classic 4. "Numerical linear algebra", Tracy Füssen. Lloyd a

Professor Zhang Zhihua: machine learning--a love of statistics and computationEditorial press: This article is from Zhang Zhihua teacher in the ninth China R Language Conference and Shanghai Jiaotong University's two lectures in the sorting out. Zhang Zhihua is a professor of computer science and engineering at Shanghai Jiaotong University, adjunct professor of d

Hello everyone, I am mac Jiang, today and everyone to share the coursera-ntu-machine learning Cornerstone (Machines learning foundations)-Job four q13-20 MATLAB implementation. The previous code was implemented through C + +, but found that C + + implementation of the code is too cumbersome, the job also to change the

Hello everyone, I am mac Jiang, today and everyone to share the coursera-ntu-machine learning Cornerstone (Machines learning foundations)-Job four q13-20 MATLAB implementation.Once the code is implemented through C + +. However, it is too cumbersome to discover that C + + implements this code. This job also need to cha

Experimental purposes Recently intend to systematically start learning machine learning, bought a few books, but also find a lot of practicing things, this series is a record of their learning process, from the most basic KNN algorithm began; experiment Introduction Language: Python GitHub Address: LUUUYI/KNNExperiment

The course covers technology:Gradient descent, linear regression, supervised/unsupervised learning, classification/logistic regression, regularization, neural network, gradient test/numerical calculation, model selection/diagnosis, learning curve, evaluation metric, SVM, K-means clustering, PCA, Map Reduce Data Parallelism, etc...The course covers applications:Message classification, tumor diagnosis, handw

regression as shown below, (note that in matlab the vector subscript starts at 1, so the theta0 should be theta (1)).MATLAB implementation of the logistic regression the function code is as follows:function[J, Grad] =Costfunctionreg (Theta, X, y, Lambda)%costfunctionreg Compute Cost andgradient for logistic regression with regularization% J=Costfunctionreg (Theta, X, y, Lambda) computes the cost of using%

);4. Simulation TestT_sim = Sim (net,p_test);5. Inversion of dataT_sim = Mapminmax (' reverse ', t_sim,ps_output);V. Performance evaluation1. Absolute error errorsError = ABS (t_sim-t_test)./t_test;2. Decision Factor r^2R2 = (n * SUM (t_sim. * t_test)-sum (t_sim) * SUM (t_test)) ^2/((N * SUM ((T_sim). ^2)-(SUM (T_sim)) ^2) * (n * SUM (t_test) . ^2)-(SUM (t_test)) ^2));3. Comparison of resultsresult = [t_test ' T_sim ' ERROR ']result = 28.1600 31.1291 0.1054 52.6500 53.0643 0.0079

Https://www.coursera.org/learn/machine-learning/exam/dbM1J/octave-matlab-tutorial Octave Tutorial 5 questions 1.Suppose I first execute the following Octave commands: A = [1 2; 3 4; 5 6]; B = [1 2 3; 4 5 6]; Which of the following is then valid Octave commands? Check all, apply and assume all options is written in an Octave command.