Probabilistic Graphical Models Coursera
Probabilistic graphical models (PGMs) are powerful tools used in machine learning and artificial intelligence for describing complex relationships between variables. PGMs are represented by graphs that allow for the efficient representation of probability distributions. PGMs allow for the modeling of uncertainty, by estimating probabilities for various outcomes based on observed variables. Coursera offers an online course on Probabilistic Graphical Models. This course is divided into three parts, introducing concepts and techniques used in PGMs such as Bayesian approaches, Markov Networks, Probabilistic Inference, Markov Decision Processes, and Monte Carlo Methods. Students will also learn about variations of PGMs like dynamic Bayesian networks, influence diagrams, and continuous PGM models. The course will also discuss the applications of PGMs in various areas such as medical diagnostics, robotics, recommender systems, and natural language processing. The course (taught by Professors Daphne Koller and Nir Friedman) is suitable for all levels of students with a basic working knowledge of probability theory and calculus. Prospective students should have some basic familiarity with linear algebra, multivariable calculus, and programming (in Python) being preferred. After completing the course, students can apply their acquired knowledge to AI and ML-related tasks, to gain a fundamental understanding of PGM models and to apply them in the real world.
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PROBABILISTIC GRAPHICAL MODELS | COURSERA
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PROBABILISTIC GRAPHICAL MODELS | COURSERA
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