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Daphne Koller

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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Three courses

Probabilistic Graphical Models 1: Representation

Probabilistic graphical models (PGMs) encode probability distributions over complex domains. They are used in medical diagnosis, image understanding, speech recognition, natural language processing, and more. This course describes Bayesian Networks and Markov networks, the two basic PGM representations.

Probabilistic Graphical Models 2: Inference

Probabilistic graphical models (PGMs) encode probability distributions over complex domains, enabling efficient probabilistic inference. This course focuses on inference algorithms, both exact and approximate, for answering questions about PGM-represented distributions. Hands-on programming assignments (honors track) implement key routines of these algorithms for real-world applications.

Probabilistic Graphical Models 3: Learning

Probabilistic graphical models (PGMs) are a framework for encoding probability distributions over complex domains. They are used in a variety of applications, such as medical diagnosis, image understanding, speech recognition, and natural language processing. This course addresses the question of learning: how a PGM can be learned from a data set of examples.

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