Submission information
Submission Number: 160
Submission ID: 3851
Submission UUID: 50ce4508-b453-465a-9229-8d1994443e22
Submission URI: /form/resource
Created: Thu, 07/20/2023 - 12:51
Completed: Thu, 07/20/2023 - 12:51
Changed: Fri, 03/14/2025 - 11:43
Remote IP address: 73.243.81.95
Submitted by: Ofer Dagan
Language: English
Is draft: No
Webform: Knowledge Base Resources
Approved: Yes
Title: Introduction to Probabilistic Graphical Models
Category: Learning
Skill Level:
Beginner (304), Intermediate (305)
Description:
This website summarizes the notes of Stanford's introductory course on
probabilistic graphical models.
It starts from the very basics and concludes by explaining from first
principles the variational auto-encoder, an important probabilistic model
that is also one of the most influential recent results in deep learning.
Link to Resource:
- https://ermongroup.github.io/cs228-notes/ (https://ermongroup.github.io/cs228-notes/)
Tags:
ai (271), machine-learning (272)
Domain:
ACCESS CSSN (780), Campus Champions (572), CAREERS (323), CCMNet (835), Great Plains (311), Kentucky (322), Northeast (308)
Would you like to associate this resource with an Affinity Group?: {Empty}