Learn about decision trees and the ID3 heuristic algorithm in this 48-minute machine learning lecture that covers fundamental concepts like data representation, tree construction for shape classification, expressivity, numeric attributes, and decision boundaries. Explore the historical development of decision tree research while mastering the basic principles and implementation of the ID3 algorithm for building effective decision trees. Gain hands-on experience through practical examples and access supplementary course materials to deepen your understanding of this essential machine learning technique.
Decision Trees and ID3 Learning Algorithm - Lecture 3
Overview
Syllabus
Intro
Key issues in machine learning
Coming up... (the rest of the semester)
This lecture: Learning Decision Trees
Representing data
What are decision trees?
Let's build a decision tree for classifying shapes
Expressivity of Decision trees
Numeric attributes and decision boundaries
Summary: Decision trees
History of Decision Tree Research
Basic Decision Tree Learning Algorithm
Basic Decision Tree Algorithm: ID3
Taught by
UofU Data Science
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