Coursera – Probabilistic Graphical Models Specialization
Free Download Coursera – Probabilistic Graphical Models Specialization
Last updated 9/2025
By Daphne Koller Professor - Stanford University
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + subtitle | Duration: 94 Lessons ( 23h 24m ) | Size: 2.14 GB
Probabilistic Graphical Models. Master a new way of reasoning and learning in complex domains
What you'll learn
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.
Applied Learning Project
Through various lectures, quizzes, programming assignments and exams, learners in this specialization will practice and master the fundamentals of probabilistic graphical models. This specialization has three five-week courses for a total of fifteen weeks.
Skills you'll gain
Applied Machine Learning
Bayesian Network
Decision Support Systems
Markov Model
Probability Distribution
Machine Learning Algorithms
Computational Thinking
Machine Learning
Probability & Statistics
Natural Language Processing
Statistical Methods
Statistical Inference
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.
Applied Learning Project
Through various lectures, quizzes, programming assignments and exams, learners in this specialization will practice and master the fundamentals of probabilistic graphical models. This specialization has three five-week courses for a total of fifteen weeks.
Homepage
https://www.coursera.org/specializations/probabilistic-graphical-models
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