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.
阅读更多.
此资源由附属合作伙伴提供。 如果您支付培训费用，我们可能会赚取佣金来支持该网站。
The techniques and tools covered in Probabilistic Graphical Models 2: Inference are most similar to the requirements found in 数据科学家 data science job advertisements.
