Probabilistic Graphical Models
About this Specialization
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.Created by: Stanford University
Related Online Courses
Course Description The Security Exam Preparation Course is a series of online courses covering topics including Linux and Windows OS basics and operations, network fundamentals, host security,... more
This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those... more
Resilience Engineering and Leadership in Crisis examines the qualities and practices of leadership amid conditions of uncertainty, chaos, or catastrophic system breakdowns. Within the complex 21st... more
This course offers a proven framework for crafting and delivering impactful presentations. In the professional world, academic settings, or public life, we\'re frequently asked to \"share some... more
This is the second course in the Marketing with TikTok specialization. In this course, you will dive into why and how businesses use TikTok for marketing. You will learn how to set up a business... more