[Coursera] Probabilistic Graphical Models (Week #1)

Stanford CS228: Probabilistic graphical models (and 隔壁的CS229是著名的ML)
老师： Professor Koller (phd from Berkeley)

摘抄课程的小广告
The goal of this class is twofold: to bring across the foundational ideas and concepts behind probabilistic graphical models, and importantly, to equip you with the tools to use and apply these models to the projects that you care about. To accomplish these goals, we have prepared a rich set of materials, which include video modules that cover many of the key ideas in the field of PGMs, each augmented with quizzes to help reinforce your knowledge.

作业：
We have also prepared a series of 9 programming assignments, each focused on a real-world application of probabilistic graphical models, from genetic counseling to recognizing human actions with the Xbox Kinect.

第一周的内容：
Bayesian Network Fundamentals (Week 1)
Semantics & Factorization
Reasoning Patterns
Flow of Probabilistic Influence
Conditional Independence
Independencies in Bayesian Networks
Naive Bayes


这门课的作业提交方法跟其他coursera的课有点不同。当你提交了作业之后可以马上看到小部分成绩，剩余的大部分成绩要在过了due date之后才公布。 这样就不知道哪里做错了，很不习惯呀。。。

第一次作业

LZ在编程作业上花了多长时间呀?　我也在考虑跟,　不过若是时间花太久,　我就只能看看视频了.

感觉这门课花的时间略多。。。犹豫中

我在跟这门课, 听说很给力, 但是也很花时间, 最近也在忙申请, 不知道最后能不能跟下去

很好的课，我们以前老师天天课堂上装逼说马尔科夫链、贝叶斯网络，这节课都有，当然啦，这不是很容易的内容

好资料 很有用 顶一个

抽时间学学。。。。总归是有用的。。

哈哈 我们老师上课也喜欢装逼，但从来讲不清楚，这回看看真货，希望能跟上。

这门课来晚了只得了一半的分。。