This course covers the learning outcomes and goals of understanding support vector machines, margin classifiers, linear separators, measuring distances, equivalent optimization, dual representation, Lagrangian, inner minimization, and classification. The course teaches the skills of implementing support vector machines and utilizing them for classification tasks. The teaching method involves theoretical explanations and practical examples. The intended audience for this course includes students and professionals interested in machine learning and data science.
Overview
Syllabus
Introduction
What are support vector machines
Margin classifiers
Linear separators
Measuring distances
Equivalent optimization
Support vector machines
Dual representation
Lagrangian
Inner minimization
Summary
Classification
Taught by
Pascal Poupart
