Learn about maximum a posteriori and maximum likelihood learning criteria through detailed examples in this comprehensive lecture on Bayesian learning, part of a machine learning course series. Explore fundamental concepts and practical applications while diving deep into probabilistic learning approaches. Access additional resources and lecture materials through the provided course website to enhance understanding of these advanced machine learning concepts.
Bayesian Learning: Maximum A Posteriori and Maximum Likelihood - Lecture 24
Overview
Syllabus
Machine Learning: Lecture 24a: Bayesian learning (continued)
Taught by
UofU Data Science
Related Courses
-
Bayesian Learning and Maximum Likelihood Methods - Lecture 23
-
Bayesian Learning and Maximum Likelihood Methods - Lecture 23
-
Bayesian Statistics: Mixture Models
-
Maximum Likelihood Estimation for Regression in Machine Learning - Lecture 24
-
Introduction to Scientific Machine Learning
-
Probabilistic Deep Learning with TensorFlow 2
