andrew ng stanford machine learning

equal to 0.3. Optimal interval classifierThe optimal interval classifier can be defined asSo set its limit toSo its LaGrand day operator isThe derivation of its factors is obtained by:ObtainedIt is possible to differentiate its factor B by:The (9) type (8) can beAnd then by the (10) type of generationSo the dual optimization problem can be expressed as:The problem of dual optimization can be obtained, so that the Jiewei of B can be obtained by (9).For a new data point x, you can make prediction

9. Clustering Content 9. Clustering 9.1 Supervised learning and unsupervised learning 9.2 K-means algorithm 9.3 Optimization Objective 9.4 Random Initialization 9.5 Choosing the number of Clusters 9.1 Supervised learning and unsupervised learningWe have learned many machine

different lambda, the calculated training error and cross-validation error are as follows:Lambda Train error Validation error 0.000000 0.173616 22.066602 0.001000 0.156653 18.597638 0.003000 0.190298 19.981503 0.010000 0.221975 16.969087 0.030000 0.281852 12.829003 0.100000 0.459318 7.587013 0.300000 0.921760 1.000000 2.076188 4.260625 3.000000 4.901351 3.822907 10.000000 16.092213 9.945508The graphic is represented as follows:As

This topic (Machine Learning) including Single-parameter linear regression, multi-parameter linear regression, Octave tutorial, logistic regression, regularization, neural network, machine learning system design, SVM (Support Vector Machines support vector machine), clusteri

found by gradient descent: '); fprintf ('%f%f \ n ', theta (1), Theta (2));% Plot the Lin Ear fithold on; % Keep previous plot visibleplot (X (:, 2), X*theta, '-') Legend (' Training data ', ' Linear regression ') hold off% don ' t overlay Any more plots on the figure% Predict values for population sizes of 35,000 and 70,000predict1 = [1, 3.5] *theta;fprintf (' for population = 35,000, we predict a profit of%f\n ',... predict1*10000);p redict2 = [1, 7] * theta;fprintf (' for population = 70

17.1 Study of large data sets17.2 Random Gradient descent method17.3 Miniature Batch gradient descent17.4 Stochastic gradient descent convergence17.5 Online Learning17.6 mapping Simplification and data parallelism 17.1 Study of large data sets 17.2 Stochastic gradient descent method 17.3miniature Batch gradient descent 17.4 stochastic gradient descent convergence 17.5 Online learning 17.6 mapping simplification and data parallelism

Public Course address:Https://class.coursera.org/ml-003/class/index INSTRUCTOR:Andrew Ng 1. Model Representation ( Model Creation ) Consider a question: what if we want to predict the price of a house in a given area based on the house price and area data? In fact, this is a linear regression problem. The given data is used as a training sample to train it to get a model that represents the relationship between price and area (actually a functi

mean vector for the above image is: 1.2 Gaussian discriminant analysis model When we have such a classification problem, its input characteristics are continuous random variables. Then we can apply Gaussian discriminant analysis (GDA): Use a multivariate Gaussian distribution to model P (x|y), as follows: The distributions are written like this: Here, the parameters of our model are φ,σ,μ0 and μ1 (note that there are 2 different mean vectors, but only one covariance matrix). Its logarithmic

process is constantly close to the optimal solution. Because the green squares overlap too much in the diagram, the middle part of the drawing appears black, and the image on the right is the result of local amplification.Algorithm analysis 1. In the gradient descent method,the batchsize is thenumber of samples used for one iteration, and when it is M, it is the batch gradient descent, which is the random gradient drop at 1 o'clock. The experimental results show that the larger the batchs

NG Machine Learning Video notes (11)--k - means algorithm theory(Reproduced please attach this article link--linhxx)I. OverviewK-Means (K-means) algorithm, is a unsupervised learning (unsupervised learning) algorithm, its core is clustering (clustering), that is, a set of in

11.1 What to do first11.2 Error AnalysisError measurement for class 11.3 skew11.4 The tradeoff between recall and precision11.5 Machine-Learning data11.1 what to do firstThe next video will talk about the design of the machine learning system. These videos will talk about the major problems you will encounter when desi

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-dimensional dimension3 help + command is the

NG Machine Learning Video notes (ii)--Gradient descent algorithm interpretation and solving θ (Reproduced please attach this article link--linhxx) First, the interpretation gradient algorithmA gradient algorithm formula and a simplified cost function diagram, as shown in.1) Partial derivativeBy the know, at point A, its partial derivative is less than 0, so θ min