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