SVM S3 MACHINE LEARNING PART 2 KTU

Welcome to the official YouTube channel for the Artificial Intelligence and Machine Learning Department at MIT!In this faculty-led session, we continue with Part 2 of Support Vector Machines (SVM) for Semester 3 students. This lecture walks step-by-step through the mathematical derivation of the SVM algorithm, showing exactly how the model optimizes the distance between hyperplanes.📌 Key Topics Covered:Deriving the SVM Decision Rule: $Y_i(W \cdot X + B) = 1$Calculating margin distances using positive and negative support vectors ($X_1$ and $X_2$)Step-by-step derivation of the geometric margin equation: Distance = $2 / ||W||$Optimization formulations: Hard Margin vs. Soft Margin (including misclassification hyperparameter 'C')A summary review of the essential diagrams required for KTU exams🎓 About This Course:Understanding the core mathematics behind Support Vector Machines is a crucial building block for anyone stepping into the world of Machine Learning, predictive modeling, and data classification.📚 More Resources:Previous Video: [Insert link to Part 1]Make sure to check our channel playlist for all Semester 3 Machine Learning lectures.👍 If you found this lecture helpful, please like the video and subscribe to the MIT AI&ML channel so you never miss an update on lectures, lab sessions, and department events!Hashtags:#MachineLearning #SVM #SupportVectorMachine #KTU #MIT #EngineeringLectures #BTech #Semester3 #TechEducation #Malayalam