Probabilistic Graphical Models
About this course
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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What you'll learn
- understand the fundamentals of probabilistic graphical models
- apply concepts from probability theory and graph algorithms
- analyze complex data interactions using PGMs
- utilize PGMs in various applications such as medical diagnosis and image understanding
Course objectives
- provide a foundational understanding of probabilistic graphical models
- demonstrate the application of PGMs in machine learning problems
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