Probabilistic Graphical Models 3: Learning
Coursera
60 Courses
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Overview
Delve into the complex world of probabilistic graphical models (PGMs) with Probabilistic Graphical Models 3: Learning, a comprehensive course offered through Coursera by Stanford University. PGMs provide a sophisticated framework for representing probability distributions across intricate domains, enabling the modeling of joint distributions over numerous variables that exhibit interconnectivity. This advanced course stands at the crossroads of statistics and computer science, incorporating elements from probability theory, graph algorithms, machine learning, and beyond to push the boundaries of applications in fields like medical diagnosis, image understanding, speech recognition, and natural language processing. As the third installment in a series, this course zeroes in on the learning aspect of PGMs, tackling the crucial challenges of parameter estimation within directed and undirected models, alongside the endeavor of structure learning in directed models. Highlighting two hands-on programming assignments for the highly recommended honors track, participants will gain practical experience by implementing and applying core routines of popular learning algorithms to tackle real-world problems. Ideal for individuals keen on exploring artificial intelligence, machine learning, data analysis, and programming, this course lights the way to mastering a key tool in formulating advanced machine learning problems.
Syllabus
Taught by
Tags
Coursera
Coursera
1275 Courses
Coursera
Free Online Course (Audit)
English
66 hours
On-Demand
