Home AI Courses Probabilistic Graphical Models 3: Learning

Probabilistic Graphical Models 3: Learning

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In addition to addressing parameter estimation and structure learning, this course covers learning probabilistic graphical models from data and contains practical programming tasks for practical use.

Key AI Functions:algorithms,graphical model,markov random field,ai & machine learning,expectation maximization algorithm

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Description for Probabilistic Graphical Models 3: Learning

Knowing Probabilistic Graphical Models (PGMs): Acquire the fundamental principles of probabilistic graphical models (PGMs), which integrate probability theory, graph algorithms, and machine learning to depict intricate probability distributions among random variables.
Methods for Parameter Estimation: Acquire understanding of the fundamental challenges associated with parameter estimation in both directed and undirected probabilistic graphical models, crucial for deriving knowledge from datasets.
Learning Structure for Directed Models: Investigate the structure learning task for directed models, an essential component in the development of precise probabilistic models.
Practical Programming Assignments: Participate in two practical programming assignments in the honors track, where you will execute essential routines from learning algorithms and apply them to real-world challenges.

Level: Advanced

Certification Degree: Yes

Languages the Course is Available: 22

Offered by: On Coursera provided by Stanford University

Duration: 3 weeks at 22 hours a week

Schedule: Flexible

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