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27 lines
1.9 KiB
Markdown
# UCB CS126 : Probability theory
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## Descriptions
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- Offered by: UC Berkeley
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- Prerequisites: CS70, Calculus, Linear Algebra
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- Programming Languages: Python
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- Difficulty: 🌟🌟🌟🌟🌟
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- Class Hour: 100 hours
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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.
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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.
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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.
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In addition to the Homework, nine Labs will allow you to use probability theory to solve practical problems in Python.
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## Course Resources
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- Course Website: <https://inst.eecs.berkeley.edu/~ee126/fa20/content.html>
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- 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)
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- Assignments: refer to the course website.
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## Personal Resources
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All the resources and assignments used by @PKUFlyingPig in this course are maintained in [PKUFlyingPig/EECS126 - GitHub](https://github.com/PKUFlyingPig/EECS126)
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