stanford university machine learning coursera

Open Course address: https://class.coursera.org/ml-003/class/index INSTRUCTOR: Andrew Ng1. unsupervised learning introduction (Introduction to unsupervised learning) We mentioned one of the two main branches of machine learning-supervised learning. Now we need to start

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

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

classifier will be severely affected, as shown in:To solve the above two problems, we adjust the optimization problem to:Note: When ξ>1, it is possible to allow the classification to be wrong, and then we add the ξ as a penalty to the target function.Using Lagrange duality again, we get the duality problem as:Surprisingly, after adding the L1 regularization item, only a αi≤c is added to the like limit in the dual problem. Note that the b* calculation needs to be changed (see Platt's paper)KKT d

Recently saw a relatively good machine learning course, roughly heard it again. The overall sense of machine learning field is still more difficult, although Li Hongyi teacher said is very good, not enough to absorb up or have a certain difficulty. Even though the process has been told, it is difficult to understand ho

length of 20. Now the machine has 8 GB of memory. How can this problem be solved. Iii. System Design Questions Forward maximum matching algorithm (FMM) for Chinese Word Segmentation in natural language processing ). Note: The example explains the basic idea of FMM. (1) design the data structure struct dictnote of the dictionary. (2) Use C/C ++ to implement FMM. The optional interface is Int FMM (vector Here, iletters is the sentence to be segmented,