udacity machine learning

distribution with the mean value of μ 0 and the covariance matrix of Σ, X | y = 1 follows the multivariate Gaussian distribution where the mean value is μ1 and the covariance matrix is Σ (This will be discussed later ). The log function for maximum likelihood estimation is recorded as L (ø, μ 0, μ 1, Σ) = Log 1_mi = 1 p (x (I) | Y (I); μ 0, μ 1, Σ) P (Y (I); ø), our goal is to obtain the parameter ø, μ 0, μ 1, Σ to make L (ø, μ 0, 1, Σ) to obtain the maximum value. The values of the four para

discriminant models (discriminative model)The generation method is obtained by the data Learning Joint probability distribution P (x, y) and then the conditional probability distribution P (y| X) as the predictive model, the model is generated : P (Y |X )= P(X,Y)p ( X ) This method is called a build method , which represents the generation relationship of output y produced by a given input x. such as: Naive Bayesian and Hidden M

Original writing, reproduced please indicate the source of http://www.cnblogs.com/xbinworld/archive/2013/04/25/3041505.html Today I will start learning pattern recognition and machine learning (PRML), Chapter 1.2, probability theory (I) This section describes the essence of probability theory in the entire book, highlighting an uncertainty understanding

Naive BayesianThis course outline:1. naive Bayesian- naive Bayesian event model2. Neural network (brief)3. Support Vector Machine (SVM) matting – Maximum interval classifierReview:1. Naive BayesA generation learning algorithm that models P (x|y).Example: Junk e-mail classificationWith the mail input stream as input, the output Y is {0,1},1 as spam, and 0 is not junk e-mail.Represents the message text as an

Hello everyone, I am mac Jiang, today and you share the coursera-ntu-machine learning Cornerstone (Machines learning foundations)-job four of the exercise solution. I encountered a lot of difficulties in doing these topics, when I find the answer on the Internet but can not find, and Lin teacher does not provide answers, so I would like to do their own questions

Part I: ClassificationThe first two parts of the book focus on supervised Learning (supervisedieaming). In the process of supervising learning, we only need to give the input sample set , and the machine can push the possible results of the specified target variable from it. Supervised learning is relatively simple, an

This series of blogs records the Stanford University Open Class-Learning notes for machine learning courses.Machine learning DefinitionArthur Samuel (1959): Field of study that gives computers the ability to learn without being explicitly programmed.Tom Mitchell (1998): A computer program was said to learn from experie

In recent years, with the rise of big data, cloud computing, mobile Internet, artificial intelligence technology, "machine learning" has become a hot term in the industry. From the field of communication Internet experts, to a variety of enterprises, and even ordinary people, the "machine learning" technology knows. So

Reprinted please indicate Source Address: http://www.cnblogs.com/xbinworld/archive/2013/04/21/3034300.html Pattern Recognition and machine learning (PRML) book learning, Chapter 1.1, introduces polynomial curve fitting) The doctor is almost finished. He will graduate next year and start preparing for graduation this year. He feels that he has done a lot of

Original writing. For more information, see http://blog.csdn.net/xbinworld,bincolumns. Pattern Recognition and machine learning (PRML) book learning, Chapter 1.1, introduces polynomial curve fitting) The doctor is almost finished. He will graduate next year and start preparing for graduation this year. He feels that he has done a lot of research on

Draw a map, there is the wrong place to welcome correct:In machine learning, features are critical. These include the extraction of features and the selection of features. They are two ways of descending dimension, but they are different:feature extraction (Feature Extraction): creatting A subset of new features by combinations of the exsiting features. In other words, after the feature extraction A feature

Dialogue machine learning Great God Yoshua Bengio (Next)Professor Yoshua Bengio (Personal homepage) is one of the great Gods of machine learning, especially in the field of deep learning. Together with Geoff Hinton and Professor Yann LeCun (Yan), he created the deep

Original: Image classification in 5 MethodsAuthor: Shiyu MouTranslation: He Bing Center Image classification, as the name suggests, is an input image, output to the image content classification of the problem. It is the core of computer vision, which is widely used in practice. The traditional method of image classification is feature description and detection, such traditional methods may be effective for some simple image classification, but the traditional classification method is overwhelmed

) ^2\)To break it apart, it was \ (\frac1 2 \bar{x}\) where \ (\bar{x}\) is the mean of the squares of $h _θ (x_i)? Y_i $, or the difference between the predicted value and the actual value.This function is otherwise called the "Squared error function", or "Mean squared error". The mean is halved \ ((\frac1 2) \) as a convenience for the computation of the gradient descent, as the derivative Term of the square function would cancel out the \frac1 2\ . The following image summarizes what is the c

The study of this class, I believe that generally on the statistics or logistics related courses should be known to some students. Although the knowledge involved in class is very basic, it is also very important.Based on the collection of some house price related data, the linear regression algorithm is used to forecast the house price.In order to facilitate the training deduction of the algorithm, a lot of symbols of the standard provisions, from which also learned some knowledge, later in the

How to select Super Parameters in machine learning algorithm: Learning rate, regular term coefficient, minibatch sizeThis article is part of the third chapter of "Neural networks and deep learning", which describes how to select the value of the initial hyper-parameter in the machi