Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Bayesian Learning and Maximum Likelihood Methods - Lecture 23

UofU Data Science via YouTube

Overview

Learn about probabilistic criteria that define learning through an exploration of maximum a posteriori and maximum likelihood learning principles in this 36-minute lecture from the University of Utah Data Science program. Examine practical examples that demonstrate these Bayesian learning concepts while gaining insights into probabilistic approaches to machine learning. Delve into detailed explanations and demonstrations that help build a strong foundation in probabilistic machine learning frameworks.

Syllabus

Machine learning: Lecture 23b: Bayesian learning

Taught by

UofU Data Science

Reviews

Start your review of Bayesian Learning and Maximum Likelihood Methods - Lecture 23

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.