Explore computational learning theory through a detailed lecture that delves into the fundamental concept of shattering and its relationship to VC (Vapnik-Chervonenkis) dimension - a crucial measure in statistical learning theory that helps determine the capacity of a classification model to learn from data.
Machine Learning: Shattering and VC Dimension - Lecture 17B
Overview
Syllabus
Machine Learning: Lecture 17b: Shattering and VC dimension
Taught by
UofU Data Science
Related Courses
-
The VC Dimension in Machine Learning - Lecture 18
-
Machine Learning: Understanding Hypothesis Spaces Through Shattering - Lecture 17
-
Machine Learning: Understanding VC Dimensions and Computational Learning Theory - Lecture 18
-
Learning from Data (Introductory Machine Learning course)
4.6 -
機器å¸ç¿’基石上 (Machine Learning Foundations)---Mathematical Foundations
5.0 -
Machine Learning with Python: from Linear Models to Deep Learning.
3.0
