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。课程平台：Coursera
开课学校：Stanford
课程全名：Probabilistic Graphical Models
开课时间：开了3轮了，最近的一轮是2013的，往后什么时候开还不知道，但是可以去看archived course
课程链接：coursera上一搜就有了，这点能力肯定所有人都有的。
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？)
正如课程介绍页面说的，一周15-20小时，绝对名副其实。有些内容消化起来确实吃力。我是数学专业的学生，之前学过edx的人工智能，
stanford的机器学习，columbia的natural language processing，这门课绝对是最难的。我学这门课纯碎处于兴趣，因为我以后希望去读
CS的master，尤其是maching learnig方面的。要说代码量到不多，因为很多的skeleton code都是提供了的，但是需要理解那些代码还是需要时间。
作业就是用matlab/octave。
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！）
缺点：有些作业本身有bug，需要借助论坛的帮助才能解决，这一点真的很不爽，会多花不少时间。好在论坛有好多热心人。
课程概述What are Probabilistic Graphical Models? Uncertainty is unavoidable in real-world applications: we can almost never predict with certainty what will happen in the future, and even in the present and the past, many important aspects of the world are not observed with certainty. Probability theory gives us the basic foundation to model our beliefs about the different possible states of the world, and to update these beliefs as new evidence is obtained. These beliefs can be combined with individual preferences to help guide our actions, and even in selecting which observations to make. While probability theory has existed since the 17th century, our ability to use it effectively on large problems involving many inter-related variables is fairly recent, and is due largely to the development of a framework known as Probabilistic Graphical Models (PGMs). This framework, which spans methods such as Bayesian networks and Markov random fields, uses ideas from discrete data structures in computer science to efficiently encode and manipulate probability distributions over high-dimensional spaces, often involving hundreds or even many thousands of variables. These methods have been used in an enormous range of application domains, which include: web search, medical and fault diagnosis, image understanding, reconstruction of biological networks, speech recognition, natural language processing, decoding of messages sent over a noisy communication channel, robot navigation, and many more. The PGM framework provides an essential tool for anyone who wants to learn how to reason coherently from limited and noisy observations. In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques; you will also learn algorithms for using a PGM to reach conclusions about the world from limited and noisy evidence, and for making good decisions under uncertainty. The class covers both the theoretical underpinnings of the PGM framework and practical skills needed to apply these techniques to new problems.
授课大纲Topics covered include: - The Bayesian network and Markov network representation, including extensions for reasoning over domains that change over time and over domains with a variable number of entities
- Reasoning and inference methods, including exact inference (variable elimination, clique trees) and approximate inference (belief propagation message passing, Markov chain Monte Carlo methods)
- Learning parameters and structure in PGMs
- Using a PGM for decision making under uncertainty.
There will be short weekly review quizzes and programming assignments (Octave/Matlab) focusing on case studies and applications of PGMs to real-world problems: - Credit Scoring and Factors
- Modeling Genetic Inheritance and Disease
- Markov Networks and Optical Character Recognition (OCR)
- Inference: Belief Propagation
- Markov Chain Monte Carlo and Image Segmentation
- Decision Theory: Arrhythmogenic Right Ventricular Dysplasia
- Conditional Random Field Learning for OCR
- Structure Learning for Identifying Skeleton Structure
- Human Action Recognition with Kinect
To prepare for the class in advance, you may consider reading through the following sections of the textbook (discount code DKPGM12) by Daphne and Nir Friedman: - Introduction and Overview. Chapters 1, 2.1.1 - 2.1.4, 4.2.1.
- Bayesian Network Fundamentals. Chapters 3.1 - 3.3.
- Markov Network Fundamentals. Chapters 4.1, 4.2.2, 4.3.1, 4.4, 4.6.1.
- Structured CPDs. Chapters 5.1 - 5.5.
- Template Models. Chapters 6.1 - 6.4.1.
These will be covered in the first two weeks of the online class.
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)
我学的时候没有看书（不是强制要看的），不过我觉得有时间的话还是推荐要看。毕竟书中的信息量会密的多，有些东西
光看lecture和ppt是没办法很好的传授的。感觉所有的MOOC配合好实体书才能发挥最大的威力。
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)
这一年跟过的MOOC少说也有十几门了，这门课最大的特点就是难，非常难，如果没有充足的时间精力和兴趣的话，慎选！
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）
前面也说了，推荐看Koller写的书（电子版google是搜的到的）。
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