Stanford cs229 manchine learning course has more mathematical requirements and formula derivation than machine learning in Coursera. The course is all in English, and the basic materials have not been translated. This basic material is mainly divided into linear algebra and probability theory, and is optimized for machine learning courses, which is very suitable for learning. I have finished translating the linear algebra part. Recently, Dr. Zhenyu Shi has finished translating the probability theory part. I modified it and put it on github for download. (Huang Haiguang)
This article is a Chinese translation of the basic materials of the CS 229 machine learning course at Stanford University.
Translation: Linear Algebra (Huang Haiguang), Probability Theory (Dr. Shi Zhenyu)
Review and revision: Huang Haiguang
The Stanford cs229 manchine learning course has more mathematical requirements and formula derivation than the machine learning in Coursera. The course is all in English, and the content of the course has been translated ( https://github.com/Kivy-CN/Stanford -CS-229-CN )
However, this github has not yet translated the basic materials.
The basic materials are mainly divided into linear algebra and probability theory, and are optimized for machine learning courses, which are very suitable for learning.
At present, we have finished translating the linear algebra and probability theory, and download it on my data science github:
https://github.com/fengdu78/Data-Science-Notes/tree/master/0.math/1.CS229
Remarks: If you need to see the original English file, download the link
Download the original English file (Probability Theory): ( http://cs229.stanford.edu/summer2019/cs229-prob.pdf )
Download the original English file (linear algebra): ( http://cs229.stanford.edu/summer2019/cs229-linalg.pdf )
Probability Theory File Directory
1. The basic elements of
probability 1.1 Conditional probability and independence
2. Random variables
2.1 Cumulative distribution function
2.2 Probability mass function
2.3 Probability density function
2.4 Expectation
2.5 Variance
2.6 Some common random variables
3. Two random variables
3.1 Joint distribution and marginal distribution
3.2 Joint probability and marginal probability quality function
3.3 Joint probability and marginal probability density function
3.4 Conditional probability distribution
3.5 Bayes theorem
3.6 Independence
3.7 Expectation and covariance
4. Multiple random variables
4.1 Basic properties
4.2 Random vector
4.3 Multivariate Gaussian distribution
5. Other resources
Linear Algebra File Directory
1. Basic concepts and symbols
1.1 Basic symbols
2. Matrix Multiplication
2.1 Vector-Vector Multiplication
2.2 Matrix-Vector Multiplication
2.3 Matrix-Matrix Multiplication
3 Operations and attributes
3.1 Identity matrix and diagonal matrix
3.2 Transpose
3.3 Symmetric matrix
3.4 Matrix trace
3.5 Norm
3.6 Linear correlation and rank
3.7 Inverse of square matrix
3.8 Orthogonal matrix
3.9 Value range and zero space of matrix
3.10 Determinant
3.11 Quadratic and positive semi-definite matrices
3.12 Eigenvalues and eigenvectors
3.13 Eigenvalues and eigenvectors of symmetric matrices
4. Matrix Calculus
4.1 Gradient
4.2 Hesse Matrix
4.3 Quadratic Function and Linear Function Gradient and Hesse Matrix
4.4 Least Square Method
4.5 Determinant Gradient
4.6 Eigenvalue Optimization
The file is divided into markdown version and pdf version, screenshots of file content:
Original course file
Translated version
My level is limited and my translation is not perfect . Everyone is welcome to submit a PR to polish the language.
You are not alone in the battle!
The translated pdf and markdown files can be downloaded from my data science github:
https://github.com/fengdu78/Data-Science-Notes/tree/master/0.math/1.CS229