Data Science Career Tracks: What's the Right Path for You?

Free Live Webinar on May 31 - Register Now

dotsdots

Probabilistic Graphical Models 1: Representation

Description

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.
Read more.

This resource is offered by an affiliate partner. If you pay for training, we may earn a commission to support this site.

Career Relevance by Data Role

The techniques and tools covered in Probabilistic Graphical Models 1: Representation are most similar to the requirements found in Data Scientist job advertisements.

Similarity Scores (Out of 100)