Specialization Probabilistic Graphical Models

Probabilistic graphical models (PGMs) provide a rich framework for encoding probability distributions in complex domains: joint (multivariate) distributions over a large number of random variables that interact with each other. These representations are at the intersection of statistics and computer science, drawing on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for state-of-the-art methods in a wide variety of applications, such as medical diagnostics, image understanding, speech recognition, natural language processing, and many, many others. They are also a fundamental tool in the formulation of many machine learning problems. Applied learning project Through a variety of lectures, quizzes, programming assignments, and exams, students in this specialization will practice and master the fundamentals of probabilistic graphical models. This specialization includes three five-week courses for a total of fifteen weeks.