Probabilistic Graphical Models 1: Representation
Coursera
60 Courses
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Overview
Discover the foundational principles of Probabilistic Graphical Models (PGMs) with "Probabilistic Graphical Models 1: Representation," offered by Stanford University through Coursera. This course provides an in-depth look at the rich framework of PGMs, a critical intersection of statistics and computer science, which leverages probability theory, graph algorithms, machine learning, and more. Designed as the first part of a three-course sequence, it covers the essentials of Bayesian Networks and Markov networks, their theoretical underpinnings, and practical applications.
PGMs serve as the backbone for cutting-edge methods across a broad range of fields including medical diagnosis, image understanding, speech recognition, natural language processing, and beyond. This course not only introduces learners to the basic representations within PGMs but also explores important extensions that allow for the encoding of more complex models efficiently. Participants interested in hands-on experience will find the optional honors track particularly beneficial, as it includes several assignments that apply these concepts to real-world problems.
Falling under multiple categories such as Artificial Intelligence, Statistics & Probability, Machine Learning, and Computer Science Courses, this offering is quintessential for anyone looking to deepen their understanding of intricate probabilistic models and their application in technology today.
Syllabus
Taught by
Tags
Coursera
Coursera
1449 Courses
Coursera
Free Online Course (Audit)
English
67 hours
On-Demand
