From 1e459978891c9a24e92c533a084b06e4ee1d3372 Mon Sep 17 00:00:00 2001 From: smxm <695335574@qq.com> Date: Sun, 25 Sep 2022 19:07:17 +0800 Subject: [PATCH] Update CS126.en.md --- docs/数学进阶/CS126.en.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/数学进阶/CS126.en.md b/docs/数学进阶/CS126.en.md index 767e05da..8743b62a 100644 --- a/docs/数学进阶/CS126.en.md +++ b/docs/数学进阶/CS126.en.md @@ -8,20 +8,20 @@ - Difficulty: 🌟🌟🌟🌟🌟 - Class Hour: 100 hours -Berkeley's advanced probability course, which involves relatively advanced theoretical content such as statistics and stochastic processes, requires certain mathematical foundation. But as long as you stick with it you will certainly take your mastery of probability theory to a new level. +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. +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 Homeworks, there are 9 Labs in this course which will allow you to use probability theory to solve actual problems. +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][Jupyter_Notebook] +- 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: see course website for specific requirements. ## Personal Resources -All the resources and assignments used by @PKUFlyingPig in this course are maintained in [PKUFlyingPic/EECS126 - GitHub](https://github.com/PKUFlyingPig/EECS126) \ No newline at end of file +All the resources and assignments used by @PKUFlyingPig in this course are maintained in [PKUFlyingPic/EECS126 - GitHub](https://github.com/PKUFlyingPig/EECS126)