nyu machine learning course

question is, how do you choose the right algorithm for your problem? Microsoft provides us with a good guide inMicrosoft Azure machine learning algorithm Cheat Sheet. This is a selection flowchart, the approximate process text is described as follows: Do you want to predict the future data points If no, then select the aggregation algorithm (only the k nearest neighbor algorithm is optional)

(i) Recognition of the returnRegression is one of the most powerful tools in statistics. Machine learning supervised learning algorithm is divided into classification algorithm and regression algorithm, in fact, according to the category label distribution type is discrete, continuity and defined. As the name implies, the classification algorithm is used for disc

example, if you use 1 million points to find an optimal hyper-plane, where there are 100 supporting vectors, then I just need to remember the information of these 100 points, and for subsequent classifications it is only necessary to use these 100 points instead of all 1 million points for calculation. Of course, in addition to the "memory-based learning" algorithm such as K-nearest neighbor, usually the a

, small sense of approval Error is like the first floor said = Bias + VarianceGenerally speaking, machine learning will choose a function space, this function space may not contain the optimal function, so even if the function space to learn that the loss of the function of the smallest one will be the real best function, the difference is bias. In addition, since we do not know the joint distribution of t

IntroductionI feel that learning machine learning algorithms is the only way to get started from a mathematical perspective, the machine learning field, the machine learning definition

Course Description:This is the last lesson of the course, the author first summed up the theory, methods, models, paradigms, and so on machine learning. Finally, the application of Bayesian theory and Aggregation (aggregation) method in machine

Many friends want to learn machine learning, but suffer from the construction of the environment, here is the Windows Scikit-learn Research and development environment to build steps.Step 1. Installation of PythonPython has versions of 2.x and 3.x, but many good machine learning Python libraries do not support 3.x, so

Many people think that machine learning is unattainable. This is a mysterious technology that only a few professional scholars know. After all, you are letting a machine running in the binary world come up with its own understanding of the real world. You are teaching them how to think. However, this article is hardly the obscure, complex, and full of mathematica

samples to establish operational knowledge. Machine Learning Machine Learning has a long history, and many textbooks have well covered its main principles. In recent textbooks, I suggest: Chris Bishop, "Pattern Recognition and machine

Machine learning notes (b) univariate linear regression Note: This content resource is from Andrew Ng's machine learning course on Coursera, which pays tribute to Andrew Ng. Model representationHow to solve the problem of house price in note (a), this will be

Long time no See, Hulu machine learning questions and Answers series and updated again!You can click "Machine Learning" in the menu bar to review all the previous installments of this series and leave a message to express your thoughts and ideas, and perhaps see your testimonials in the next article.Today's theme is"Di

The article is from Professor Andrew Ng of Stanford University's machine learning course, which is a personal study note for the course, subject to the contents of the original course. Thank Bo Master Rachel Zhang's personal notes, for me to do personal study notes provide a

million points to find an optimal hyper-plane, where there are 100 supporting vectors, then I just need to remember the information of these 100 points, and for subsequent classifications it is only necessary to use these 100 points instead of all 1 million points for calculation. Of course, in addition to the "memory-based learning" algorithm such as K-nearest neighbor, usually the algorithm does not dire