Overview
Learn about Bayesian Learning concepts through a comprehensive lecture that covers fundamental principles and practical applications. Explore key topics including prior probability, model definition, and naive classifiers through an engaging tennis example. Dive deep into base classifier implementation, binary labels, and the general case framework for classification problems. Master essential concepts in data science and machine learning through detailed explanations and practical demonstrations that bridge theoretical understanding with real-world applications.
Syllabus
Introduction
Bayesian Learning
Tennis Example
Prior Probability
General Case
Base Classifier
Model Definition
Naive Classifier
Binary Labels
Taught by
UofU Data Science