关于此 专项课程

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Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.
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设置并保持灵活的截止日期。
高级
完成时间大约为4 个月
建议 11 小时/周
英语(English)
字幕:英语(English)
学生职业成果
50%
完成此 专项课程 后开始了新的职业。
20%
加薪或升职。
可分享的证书
完成后获得证书
100% 在线课程
立即开始,按照自己的计划学习。
灵活的计划
设置并保持灵活的截止日期。
高级
完成时间大约为4 个月
建议 11 小时/周
英语(English)
字幕:英语(English)

此专项课程包含 3 门课程

课程1

课程 1

Probabilistic Graphical Models 1: Representation

4.7
1,268 个评分
278 条评论
课程2

课程 2

Probabilistic Graphical Models 2: Inference

4.6
440 个评分
63 条评论
课程3

课程 3

Probabilistic Graphical Models 3: Learning

4.6
270 个评分
41 条评论

提供方

斯坦福大学 徽标

斯坦福大学

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  • This class does require some abstract thinking and mathematical skills. However, it is designed to require fairly little background, and a motivated student can pick up the background material as the concepts are introduced. We hope that, using our new learning platform, it should be possible for everyone to understand all of the core material.

    Though, you should be able to program in at least one programming language and have a computer (Windows, Mac or Linux) with internet access (programming assignments will be conducted in Matlab or Octave). It also helps to have some previous exposure to basic concepts in discrete probability theory (independence, conditional independence, and Bayes' rule).

  • For best results, the courses should be taken in order.

  • No.

  • You will be able to take a complex task and understand how it can be encoded as a probabilistic graphical model. You will now know how to implement the core probabilistic inference techniques, how to select the right inference method for the task, and how to use inference to reason. You will also know how to take a data set and use it to learn a model, whether from scratch, or to refine or complete a partially specified model.

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