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- Difficulty:🌟🌟🌟🌟
- Class Hour: 100 hours
Another course offered by Andrew Ng, but this is a postgraduate course, so it focuses more on mathematical theory, if you are not satisfied with off-the-shelf, and want to understand the essence of the algorithm, or aspire to engage in theoretical research on machine learning, you can take this course. All the lecture notes are provided on the course website, which are professional and theoretical, and require a solid mathematics skills.
This is another ML course offered by Andrew Ng. Since it is graduate-level, it focuses more on the mathematical theory behind machine learning. If you are not satisfied with using off-the-shelf tools but want to understand the essence of the algorithm, or aspire to engage in theoretical research on machine learning, you can take this course. All the lecture notes are provided on the course website, written in a professional and theoretical way, requiring a solid mathematical background.
## Resources
- Course Website: <http://cs229.stanford.edu/syllabus.html>
- Recordings: <https://www.bilibili.com/video/BV1JE411w7Ub>
- Recordings: None, but the lecture notes is excellent.
- Assignments: Not open to the public
- Textbook: None, but the lecture notes is excellent.
- Assignments: Not open to the public.
## Personal Resources
All the resources and assignments used by @PKUFlyingPig in this course are maintained in [PKUFlyingPig/CS229 - GitHub](https://github.com/PKUFlyingPig/CS229)
All the resources and assignments used by @PKUFlyingPig in this course are maintained in [PKUFlyingPig/CS229 - GitHub](https://github.com/PKUFlyingPig/CS229).