coursera introduction to machine learning

Part of the theoretical principle can be seen in this article: http://www.cnblogs.com/charlesblc/p/6109551.htmlThis is the actual combat section. Reference to the Http://www.cnblogs.com/shishanyuan/p/4747778.htmlThe algorithm of clustering, regression and collaborative filtering is used in three cases.I feel good and need to try each one in the actual system.More API Introduction can refer to http://spark.apache.org/docs/2.0.1/ml-guide.html"Todo" Spar

(especially decision-making and judgment, One copy isSimple heuristics that makes usSmart The other one isBounded Rationality: the adaptiveToolbox Different from the statistical machine learning method adopted by computer science, these two books focus more on the cognitive methods actually used by humans. The following is my introduction to the discussion gr

Reference: onestack script This article provides a summary of openstack learning in the last month, including the problem record during installation and deployment in a standalone environment and the source code learning process. It is suitable for beginners. I. openstack installation and deployment Currently, the official installation and deployment documents and the onestack script in Chinese are displaye

vision. sponsored by IEEE. iccv was held in an odd year. It used to take turns in North America, Europe, and Asia. It was originally set in Beijing in 2003. Later, it was changed for SARS and France, which was originally set to 05 years. Iccv '07 will be held in South America (Brazil) for the first time.In principle, cvpr runs in North America every year. If iccv is in North America that year, there is no cvpr in that year.Icml (1): One of the best conferences on

Machine Learning notes of the Dragon Star program 　Preface In recent weeks, I spent some time learning the machine learning course of the Dragon Star program for the next summer vacation. For more information, see the appendix. This course chooses to talk about the basic mod

models using caret.SummarizeEight commonly used machine learning algorithms are described in this article: Linear regression model Rogers regression model Linear discriminant Analysis Regularization of the regression K Nearest Neighbor Naive Bayesian Support Vector Machine Classification and regression tree From the

In Coursera Stanford Machine Learning,lecturer strongly recommended open source programming environment octave Start, so I also downloaded to try itReference Link: http://www.linuxdiyf.com/linux/22034.html******************************************************************************Installation (Ubuntu16.04): I saw the Xia Guan Web, Ubuntu has been updated to 4.0

distribution, in accordance with the joint distribution of the query, we can obtain pi.Q's design is said to be a value of 60W knife annual salary job, dare not to speculate. Here we assume that Q is given (UNIFORM/SW) **********************************************The MH sampling process is as follows:1, given assignment, according to the F to find Pi (Assignment)2, according to the above formula to calculate the acceptance probability a3, decide whether to accept, complete the sampling update

Http://blog.sina.com.cn/s/blog_6b99cdb50101ix0l.htmlOne of the math related to machine learning and computer vision(The following is a space article to be transferred from an MIT bull, which is very practical:)DahuaIt seems that mathematics is not always enough. These days, in order to solve some of the problems in the library, also held a mathematical textbook. From the university to the present, the class

tend to be worse on new data, i.e. under-fitting; The high variance can be seen as a model that fits the training set too well, and the new data will be poor, that is, over-fitting. So for the model effect, in addition to feature engineering such trick, "tune to a good argument"-to solve good bias and variance tradeoff, but also part of the core competitiveness of algorithmic engineers. But "large-scale machine