Probabilistic Graphical Models 3: Learning
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.
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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