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
In this course, you will explore the foundations upon which modern-day ESG was built, how market forces react to ESG, and ways to create and maintain value using ESG investment strategies. You will... more
Service level indicators (SLIs) and service level objectives (SLOs) are fundamental tools for measuring and managing reliability. In this course, students learn approaches for devising appropriate... more
This course is ideal for individuals who currently work in or are targeting opportunities in consulting and strategy, industrial sales and buying, marketing management, entrepreneurship and... more
This comprehensive graphic design course empowers you to stay ahead of the curve, harnessing AI tools and creating a professional portfolio that showcases your talent. Analyze competitors, explore... more
In this 1-hour long project-based course, you will create projects on Harvest, track their time and expenses, generate reports and invoice your clients.Created by: Coursera Project Network more