【长期加分贴】介绍和点评你上过的公开课
EroicaCMCS
现在网上的公开课资源很多了，但是相同或者类似的课程很多，让人很难抉择。还有很多好的资源没有被发现。
欢迎跟过公开课(不一定要全部跟完)的同学贡献信息，介绍你上过的公开课，给后来想跟课的同学一个参考，加分多多哦~~
要求包括以下内容：
1. 课程的基本信息：课程平台，开课学校，课程全名，开课时间，课程链接。
2. 课程难度，作业量，每周花在这门课的时间，以及你的基础(是初学者？还是本来就是你的专业内容？)
3. 各种感想、收获、课程内容介绍、你对这门课的评价等等~~这个就自己发挥啦（请具体！）
4. 如果让你重新学过这门课，你会在学习方法上，背景提升上会有什么样的改进(选答)
5. 这门课和其他你跟过的课，用过的资源或者是学校上过的课有什么区别？(选答，如果你还跟过其他类似的课的话)
6. 给以后打算学这门课的同学一些建议（请具体！可包括任何方面）
课程平台不限，Coursera, Udacity, edX, Stanford Online(openEdX), Stanford SEE, MIT OCW, UCB webcast, udemy, code school, codecademy, MongoDB University, NovoEd, Khan Academy等等都行，只要你跟过，不一定全部跟完，分享资讯都可以获得加分！
请大家直接回复到下面，版主会过来加分~
评论列表 (更新至220L)
欢迎跟过公开课(不一定要全部跟完)的同学贡献信息，介绍你上过的公开课，给后来想跟课的同学一个参考，加分多多哦~~
要求包括以下内容：
1. 课程的基本信息：课程平台，开课学校，课程全名，开课时间，课程链接。
2. 课程难度，作业量，每周花在这门课的时间，以及你的基础(是初学者？还是本来就是你的专业内容？)
3. 各种感想、收获、课程内容介绍、你对这门课的评价等等~~这个就自己发挥啦（请具体！）
4. 如果让你重新学过这门课，你会在学习方法上，背景提升上会有什么样的改进(选答)
5. 这门课和其他你跟过的课，用过的资源或者是学校上过的课有什么区别？(选答，如果你还跟过其他类似的课的话)
6. 给以后打算学这门课的同学一些建议（请具体！可包括任何方面）
课程平台不限，Coursera, Udacity, edX, Stanford Online(openEdX), Stanford SEE, MIT OCW, UCB webcast, udemy, code school, codecademy, MongoDB University, NovoEd, Khan Academy等等都行，只要你跟过，不一定全部跟完，分享资讯都可以获得加分！
请大家直接回复到下面，版主会过来加分~
评论列表 (更新至220L)
- Data Structures @Berkeley
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- Introduction to Algorithms @MIT
- Linear Algebra @MIT
- Human Behavioral Biology @Stanford
- Probabilistic Graphical Models @Stanford
- Intro to Computer Science @Udacity
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- Intro to Data Science @Udacity
- Programming Methodology @Stanford
- Neural Networks for Machine Learning @UToronto
- Financial Engineering and Risk Management @Columbia
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- Programming Languages @UW-Seattle
- Programming Languages @UW-Seattle
- Introduction to Computer Network @Stanford
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- Introduction to Psychology @Yale
- Natural Language Processing @Columbia
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- Algorithms @Princeton
- Networks, Crowds, and Markets @Cornell
- Machine Learning @Stanford
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- R Programming @JHU
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- Computing for Data Analysis @JHU
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- The Fourier Transform and Its Applications @Stanford
- Machine Learning @Stanford
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- Data Analysis and Statistical Inference @Duke
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- Machine Learning @Stanford
- Algorithms Design and Analysis @Stanford
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- Algorithm: Design and Analysis @Stanford
- Internet History, Technology, and Security @UMich
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- Introduction to Computer Science @Harvard
- Data Analysis and Statistical Inference @Duke
- Algorithmic Thinking @Rice
- 计算机网络概论 @台湾清华大学
- An Introduction to Interactive Programming in Python @Rice
- Principles of Computing @Rice
- Algorithmic Thinking @Rice
- Introduction to Power Electronics @Colorado
- 数据结构 @ZJU
- Software Security @UMD
- Introduction to Algorithm @MIT
- 中国古代历史与人物——秦始皇 @NTU
- PaulProgramming @YouTube
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- Algorithms: Design and Analysis @Stanford
- Introduction to Computer Science @Harvard
- Principles of Computing @Rice
- Game Theory @Stanford
- Intro do Computer Science @Udacity
- Intro to Relational Database @Udacity
- Programming Methodology @Stanford
- Introduction to Computer Science and Programming: Using Python @MIT
- Linear Algebra @MIT
- Data Structures @Tsinghua
- Statistical Mechanics: Algorithms and Computations @巴黎高师
- Algorithms @Princeton
- Machine Learning @Stanford
- Programming Languages @UW-Seattle
- Algorithms: Design and Analysis @Stanford
- Convolutional Neural Networks for Visual Recognition @Stanford
- Medical Image Analysis @SJTU
- Design and Analysis of Algorithms @MIT
- Programming for Everybody (Getting Started with Python) @UMich
- R Programming @JHU
- Introduction to Programming with Matlab @Vanderbilt
- Programming Mobile Applications for Android Handheld Systems @UMD
- R programming @JHU
- 機器學習技法 @NTU
- Financial Markets @Yale
- Introduction to Algorithms @MIT
- Object Oriented Programming in Java @UCSD
- Full Stack Web Development @HKUST
- An Introduction to Interactive Programming in Python @Rice
- An Introduction to Interactive Programming in Python @Rice
- The Analytics Edge @MIT
- Full Stack Web Development @HKUST
- Machine Learning @Stanford
- Convex Optimization @CMU
- Intro to Programming @Udacity
- Programming Methodology @Stanford
- Machine Learning @Stanford
- Introduction to Computer Network @Stanford
- Machine Learning @Stanford
- Model Thinking @UMich
- Deep learning @Udacity
- Intro to Parallel Programming @Udacity
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- Data Structures and Algorithm Design @Tsinghua
- Machine Learning @Stanford
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- Machine Learning @Stanford
- Introduction to Computer Systems @CMU
- Programming Abstractions @Stanford
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