└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Probabilistic-graphical-models 2 | 3 | ## 主要内容 4 | 5 | - [课程列表](#curriculum) 6 | - [专项课程学习路线](#special_course_learning) 7 | - [辅助书籍](#advanced_books) 8 | - [论文专区](#papers_reading) 9 | 10 | ##

课程列表

11 |   和之前的一样，此处我们建议把Notes部分内容全部学完，并且能较好的理解并完成相应网站的学习作业，关于参考书此处同样不做要求。 12 | 13 | 课程 | 机构 | 参考书 | Notes等其他资料 14 | :-- | :--: | :--: | :--: 15 | [概率图模型](https://www.youtube.com/watch?v=WPSQfOkb1M8&list=PL50E6E80E8525B59C)| Stanford | [Probabilistic Graphical Models: Principles and Techniques](https://github.com/JimmyLin192/GraphicalModel/blob/master/Probabilistic%20Graphical%20Models%20Principles%20and%20Techniques.pdf) |[链接](http://cs.stanford.edu/~ermon/cs228/index.html) 16 | 概率图模型(高级方法)| Stanford | [Machine Learning: a Probabilistic Perspective](https://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020/ref=sr_1_2?ie=UTF8&qid=1336857747&sr=8-2) |  [链接](https://sites.google.com/site/cs228tspring2012/) 17 | 18 | ##

专项课程学习路线

19 | 20 | 课程 | 机构 | 参考书 | Notes等其他资料 21 | :-- | :--: | :--: | :--: 22 | [概率图模型](https://www.youtube.com/watch?v=WPSQfOkb1M8&list=PL50E6E80E8525B59C)| Stanford | [Probabilistic Graphical Models: Principles and Techniques](https://github.com/JimmyLin192/GraphicalModel/blob/master/Probabilistic%20Graphical%20Models%20Principles%20and%20Techniques.pdf) |[链接](http://cs.stanford.edu/~ermon/cs228/index.html) 23 | 概率图模型(高级方法)| Stanford | [Machine Learning: a Probabilistic Perspective](https://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020/ref=sr_1_2?ie=UTF8&qid=1336857747&sr=8-2) |  [链接](https://sites.google.com/site/cs228tspring2012/) 24 | 25 | 26 | ##

进阶书籍

27 | 下列书籍是领域公认的较好的学习书籍列表，**至少需要完成一本书籍的阅读**，方可进入之后的论文专区。 28 | 29 | 课程 | 作者 | 难度 30 | :-- | :--: | :--: | :--: 31 | [Graphical models, exponential families, and variational inference](https://people.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdf)| Martin J. Wainwright and Michael I. Jordan. | 较难 32 | [Modeling and Reasoning with Bayesian networks](https://www.amazon.com/Modeling-Reasoning-Bayesian-Networks-Darwiche/dp/0521884381)| Adnan Darwiche | 较难 33 | [Machine Learning: a Probabilistic Perspective](https://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020/ref=sr_1_1?s=books&ie=UTF8&qid=1479202414&sr=1-1&keywords=Machine+Learning%3A+a+Probabilistic+Perspective)|Kevin P. Murphy | 较难 34 | [Information Theory, Inference, and Learning Algorithms](http://www.inference.phy.cam.ac.uk/mackay/itila/book.html)| David J. C. Mackay. |较难 35 | [Bayesian Reasoning and Machine Learning](https://www.amazon.com/Bayesian-Reasoning-Machine-Learning-Barber/dp/0521518148/ref=sr_1_1?s=books&ie=UTF8&qid=1479202565&sr=1-1&keywords=Bayesian+Reasoning+and+Machine+Learning)| David Barber | 较难 36 | 37 | 38 | 39 | ##

论文专区

40 | 恭喜您坚持到了现在，现在您已经拥有了扎实的数学功底，同时经过这么多的练习，也已经掌握了概率图模型学习中的较为经典甚至一些较为前沿的技术，接下来如果您希望继续深造并成为大师并对该领域做出突破贡献，我们唯一能为您提供的就是下面的论文平台，它汇总了最经典的领域论文，领域开源包等等丰富资源，同时会不断更新最新的进展，希望对您有帮助，补充一句：**我们强烈建议您进入高校或者其他研究所进行深造，因为现在您当前的基础已经完全可以支撑您进行进一步研究，如果有好的导师引路，加之努力，将来定会成为大师，希望继续加油！**。 41 | 42 | - [尚未完善，希望大家提供较好的学习资料] 43 | 44 | 45 | 46 | --------------------------------------------------------------------------------