One of the Stanford machine Learning implementations and analyses (foreword)

Since the end of last year to learn Andrew Ng's machine learning public class, in accordance with its courseware to try to achieve some of the algorithm to deepen understanding, but in this process encountered some problems, or for the implementation of the program, or to understand the algorithm. So prepare to organize this course and document your understanding, either right or wrong, to discuss together.

This course mainly includes three parts: supervised learning algorithm, unsupervised learning algorithm and learning theory. The supervised learning part talks about regression, generating learning algorithm and SVM, while unsupervised learning is about k-means,mog,em,pca,ica and reinforcement learning, and learning theory is the method of evaluating the algorithm, choosing the model and the feature. The order of the courses organized here will be the same as the original handout.

In addition, consider the main purpose here is to analyze and understand the algorithm, will be mainly implemented using MATLAB, to facilitate the matrix and vector operations, as well as the results display.

　　Linear regression

One of the Stanford machine Learning implementations and analyses (foreword)