Support Vector Machines and Maximizing Margins in Machine Learning - Lecture 20
Learn Excel and Financial Modeling the Way Finance Teams Actually Use Them
Learn AI, Data Science & Business — Earn Certificates That Get You Hired
Overview
Google, IBM & Meta Certificates – 40% Off
One plan covers every Professional Certificate on Coursera.
Unlock All Certificates
Explore a comprehensive lecture on Support Vector Machines (SVM) that delves into the fundamental relationship between margin maximization and linear classifier learning. Understand the SVM objective function and its role in training, while discovering the concept of regularized risk minimization. Learn through detailed explanations and examples in this 81-minute machine learning lecture from the University of Utah's Data Science program. Access supplementary materials and detailed lecture notes through the provided course website to enhance understanding of this crucial machine learning algorithm.
Syllabus
Machine Learning: Lecture 20: Support Vector Machines
Taught by
UofU Data Science