diff --git a/docs/数学基础/MITmaths.en.md b/docs/数学基础/MITmaths.en.md
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+# MIT Calculus Course
+
+## Descriptions
+
+- Offered by: MIT
+- Prerequisites: English
+- Programming Languages: None
+- Difficulty: 🌟🌟
+- Class Hour: Varying from person to person
+
+The calculus course at MIT consists of MIT18.01: Single Variable Calculus and MIT18.02: Multivariable Calculus. If you are confident in your math, you can just read the course notes, which are written in a very simple and vivid way, so that you will not be tired of doing homework but can really see the essence of calculus.
+
+In addition to the course materials, the famous Youtuber **3Blue1Brown**'s video series [The Essence of Calculus](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) are also great learning resources.
+
+## Course Resources
+
+- Course Website: [18.01](https://ocw.mit.edu/courses/mathematics/18-01sc-single-variable-calculus-fall-2010/syllabus/), [18.02](https://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/)
+- Recordings: refer to course website
+- Textbook: refer to course website
+- Assignments: refer to course website
diff --git a/docs/数学基础/MITmaths.md b/docs/数学基础/MITmaths.md
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 - 课程难度：🌟🌟
 - 预计学时：因人而异
 
-MIT 的微积分课由 MIT18.01: Single variable calculus 和 MIT18.02: Multi variable calculus 两门课组成。对自己数学基础比较自信的同学可以只看课程 notes，写得非常浅显生动并且抓住本质，让你不再疲于做题而是能够真正窥见微积分的本质魅力。
+MIT 的微积分课由 MIT18.01: Single Variable Calculus 和 MIT18.02: Multivariable Calculus 两门课组成。对自己数学基础比较自信的同学可以只看课程 notes，写得非常浅显生动并且抓住本质，让你不再疲于做题而是能够真正窥见微积分的本质魅力。
 
 配合油管数学网红 **3Blue1Brown** 的[微积分的本质](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr)系列视频食用更佳。
 
diff --git a/docs/数学进阶/CS126.en.md b/docs/数学进阶/CS126.en.md
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+# UCB CS126 : Probability theory
+
+## Descriptions
+
+- Offered by: UC Berkeley
+- Prerequisites: CS70, Calculus, Linear Algebra
+- Programming Languages: Python
+- Difficulty: 🌟🌟🌟🌟🌟
+- Class Hour: 100 hours
+
+This is Berkeley's advanced probability course, which involves relatively advanced theoretical content such as statistics and stochastic processes, so a solid mathematical foundation is required. But as long as you stick with it you will certainly take your mastery of probability theory to a new level.
+
+The course is designed by Professor Jean Walrand, who has written an accompanying textbook, [Probability in Electrical Engineering and Computer Science](https://link.springer.com/book/10.1007/978-3-030-49995-2), in which each chapter uses a specific algorithm as a practical example to demonstrate the application of theory in practice. Such as PageRank, Route Planing, Speech Recognition, etc. The book is open source and can be downloaded as a free PDF or Epub version.
+
+Jean Walrand has also created accompanying Python implementations of the examples throughout the book, which are published online as [Jupyter Notebook](https://jeanwalrand.github.io/PeecsJB/intro.html) that readers can modify, debug and run them online interactively.
+
+In addition to the Homework, nine Labs will allow you to use probability theory to solve practical problems in Python.
+
+## Course Resources
+
+- Course Website: https://inst.eecs.berkeley.edu/~ee126/fa20/content.html
+- Textbook: [PDF](https://link.springer.com/content/pdf/10.1007%2F978-3-030-49995-2.pdf), [Epub](https://link.springer.com/download/epub/10.1007%2F978-3-030-49995-2.epub), [Jupyter Notebook](https://jeanwalrand.github.io/PeecsJB/intro.html)
+- Assignments: refer to the course website.
+
+## Personal Resources
+
+All the resources and assignments used by @PKUFlyingPig in this course are maintained in [PKUFlyingPic/EECS126 - GitHub](https://github.com/PKUFlyingPig/EECS126)