概率图模型. Master a new way of reasoning and learning in complex domains
Through various lectures, quizzes, programming assignments and exams, learners in this specialization will practice and master the fundamentals of probabilistic graphical models. This specialization has three five-week courses for a total of fifteen weeks.
The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.
Can I just enroll in a single course?
Can I take the course for free?
此课程是 100% 在线学习吗？是否需要现场参加课程？
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).
Do I need to take the courses in a specific order?
For best results, the courses should be taken in order.
Will I earn university credit for completing the Specialization?
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.