Mathematics in Machine Learning

Source: Internet
Author: User
Tags svm

Here, I wrote a blog to record my learning processes and experiences.

First, let's take a rough look at the ideas and write the following articles:Article

I. Basic Mathematical knowledge necessary for Machine Learning

Next, according to Fisher's big masterpiece PRML, we listed the rough outline.

Ii. Probability Distribution: some basic probability distributions

This kind of probability distribution is the basis of machine learning.

3. Linear Classification and Regression

Baysian thought is big

Iv. Guassian distribution and kernel functions

Research in the field of statistics is still quite popular.

5. Support Vector Machine)

PS: SVM is the main research direction of my boss and the most thorough understanding of it. I will explain the relevant knowledge in detail and teach libsvm to implement basic SVM at last.Algorithm.

Vi. Graph Model

As we all know, graph models and SVM are important tools in the machine learning field. The younger brother will try to explain them clearly.

VII. EM algorithm, Gaussian Mixture Model

Not clear yet

VIII. Approximate Inference

9. HMM Model and lDs

10. Summary

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