cmu machine learning course

reduced after removing the label, (2) using the data of the reduced dimension to train the model, (3) for the new data points, the PCA reduced dimension to obtain the dimensionality reduction data, and the model to obtain the predicted value. Note : You should only use the training set data for PCA dimensionality reduction get Map $x^{(i)}\rightarrow z^{(i)}$, and then apply the mapping (PCA-selected principal matrix $u_reduce$) to the validation set and test set do not use PCA to block ove

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

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

This is what we have learned (except decision tree)Here is a typical decision tree algorithm, with four places to choose from:Then introduced a cart algorithm: By decision Stump divided into two categories, the criterion for measuring subtree is that the data are divided into two categories, the purity of these two types of data (purifying).The following is a measure of purity:Finally, when to stop:Decision tree may be overfitting, reducing the number of Ein and leaves (indicating the complexity

In this section, a linear model is introduced, and several linear models are compared, and the linear regression and the logistic regression are used for classification by the conversion error function.More important is this diagram, which explains why you can use linear regression or a logistic regression to replace linear classificationThen the stochastic gradient descent method is introduced, which is an improvement to the gradient descent method, which greatly improves the efficiency.Finally

This section is about the nuclear svm,andrew Ng's handout, which is also well-spoken.The first is kernel trick, which uses nuclear techniques to simplify the calculation of low-dimensional features by mapping high-dimensional features. The handout also speaks of the determination of the kernel function, that is, what function K can use kernel trick.In addition, the kernel function can measure the similarity of two features, the greater the value, the more similar.Next is the polynomial Kernel, w

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

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 data Science Research Center of Shanghai Jiaot

IntroductionThe systematic learning machine learning course has benefited me a lot, and I think it is necessary to understand some basic problems, such as the category of machine learning algorithms.Why do you say that? I admit th

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

watch all the course videos at any time, download handouts and notes from Stanford CS229 course. This course includes homework and small tests, which mainly explain the knowledge of linear algebra, using the Octave library. Caltech learning from data at the California Institute of Technology: You can ta

) The principle of big data Large data rationale Large amounts of data can greatly improve the final performance of the learning algorithm, rather than whether you use more advanced algorithms, etc., so there is a sentence: "It's not a who had the best algorithm that wins. It's Who's have the most data. Of course, based on the two-point premise hypothesis: 1. Assume that the characteristics of the sample ca

Liblinear instead of LIBSVM 2.Liblinear use, Java version Http://www.cnblogs.com/tec-vegetables/p/4046437.html 3.Liblinear use, official translation. http://blog.csdn.net/zouxy09/article/details/10947323/ http://blog.csdn.net/zouxy09/article/details/10947411 4. Here is an article, write good. Transferred from: http://blog.chinaunix.net/uid-20761674-id-4840097.html For the past more than 10 years, support vector machines (SVM machines) have been the most influential algorithms in

Preface: "The foundation determines the height, not the height of the foundation!" The book mainly from the coding program, data structure, mathematical theory, data processing and visualization of several aspects of the theory of machine learning, and then extended to the probability theory, numerical analysis, matrix analysis and other knowledge to guide us into the world of

rigorously, because one of the objective functions in statistical learning is to maximize the prediction of the correct expected probability, we only consider the common loss function. Loss function is an important index to approximate the quality of the model, the greater the value of the loss function is, the greater the prediction error of the model, so what we need to do is to update the parameters of the model and minimize the value of the loss

Videos CourseMany people start to learn from the machine through video resources. I saw a lot of video resources related to machine learning on YouTube and Videolectures. The problem with this is that you may just watch the video and not actually do it. My suggestion is that when you watch the video, you should take more notes, and then you will discard your not

place is different, for example, in quite a detailed introduction of neural network theory of the rise and fall. So I strongly suggest you look at yourself again and don't forget the links inside the link to other places. By the way, Xu 's classmate intends to find time to translate this article, this is a fairly long article, see the E-text waiting to see translation:)The second one is " ai " (Artificial Intelligence). Of course, there are

Learning notes for "Machine Learning Practice": two application scenarios of k-Nearest Neighbor algorithms, and "Machine Learning Practice" k- After learning the implementation of the k-Nearest Neighbor Algorithm, I tested the k-

notes2), awesome! After reading the first two parts, the third part of the bounded difference inequality has not seen. The derivation of the front from Markov to Chebyshev to Howding is very small and fresh and smooth.5/21/2016 11:20:08 PM36-705 CMU Intermediate StatisticsCourse descriptionThis course would cover the fundamentals of theoretical statistics.We'll cover chapters 1–12 from the text plusSome su

classic paper; This book can be used as a supplementary reading for each of the two books. "Machine learning" (ml) PDFAuthor Tom Mitchell is a master of CMU, with a machine learning and semi-supervised learning Network