Machine Learning for Engineering & Science Applications | IIT Madras - #91 Support Vector Machines | Machine Learning for Engineering & Science Applications

Welcome to 'Machine Learning for Engineering & Science Applications' course ! Ever wondered how machines can draw boundaries to separate different categories of data? This lecture dives into the world of Support Vector Machines (SVMs), powerful algorithms that find the optimal line or curve (the decision boundary) to neatly separate your data points. We'll explore the concept of maximizing the "margin," the space between the boundary and the closest data points, and how this leads to robust classification. Plus, we'll touch upon the clever "kernel trick" that allows SVMs to handle even complex, non-linear boundaries. Get ready to unlock the secrets of intelligent classification NPTEL Courses permit certifications that can be used for Course Credits in Indian Universities as per the UGC and AICTE notifications. To understand various certification options for this course, please visit https://nptel.ac.in/courses/106106198 #SupportVectorMachines #SVM #DecisionBoundaries #LinearlySeparable #SupportVectors #Margin #CostFunction #KernelTrick