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国外课程作业实现可见我的 Github repository：SLS: Self Learning stack.

国外课程、教材资源

（不难发现，我偏爱 Stanford University 😑）

AI 类课程

CS231n: Deep Learning for Computer Vision, Stanford University

• 这门课程是我「梦开始的地方」

• Course website: http://cs231n.stanford.edu

CS224n: Natural Language Processing with Deep Learning, Stanford University.

• 与 CS231n 相应，核心主题是 NLP。
• Course website: https://web.stanford.edu/class/cs224n/index.html

CS229: Machine Learning, Stanford University.

• Lecture Notes 极为丰富，该课程的作业可通过一个 trick 得到：http://cs229.stanford.edu/summer2020/

• Course website: http://cs229.stanford.edu

CS 189: Introduction to Machine Learning, UC Berkeley

• 这门课的 Lecture Notes 内容亦很丰富，可以与 CS229 相结合食用。
• Course website: https://www.eecs189.org

Understanding Machine Learning, Cambridge University

• 这本书更偏 Learning Theory，是我接触 Machine Learning 的第一本书。

• https://www.cs.huji.ac.il/w~shais/UnderstandingMachineLearning/

STATS214 / CS229M: Machine Learning Theory, Stanford University

• 机器学习进阶课程，极其硬核，我仅用其作「陶冶情操」。
• Course website: https://web.stanford.edu/class/stats214/

其他计算机类课程

CS142: Web Applications, Stanford University.

• Course website: https://web.stanford.edu/class/cs142/index.html

CS148: Introduction to Computer Graphics and Imaging, Stanford University

• Course website: https://web.stanford.edu/class/cs148/lectures.html

物理数学类课程

Physics342: Quantum Mechanics I, Reed University

• 作为量子力学入门，我建议学习下面这本书
• Material: “Introduction to Quantum Mechanics, 3rd ed.”, Cambridge University Press, 2018.

15-458: Discrete Differential Geometry, Carnegie Mellon University

• 离散微分几何学，可以作为计算机图形学进阶数学知识学习。
• Course website: https://csd.cmu.edu/course-profiles/15-458-Discrete-Differential-Geometry