Last Minute Lecture

Support Vector Machines, Kernel Methods, and Nonlinear Classification Explained | Chapter 7 of Why Machines Learn Chapter 7, “The Great Kernel Rope Trick,” from Why Machines Learn: The Elegant Math Behind Modern AI traces the invention of Support Vector Machines (SVMs) and the mathematical breakthrough that made them one of the most powerful algorithms of the 1990s and early 2000s. Anil Ananthaswamy weaves together geometry, optimization, and historical insight to show how SVMs transformed machine learning by solving nonlinear classification problems with elegance and efficiency. This post expands on the chapter, offering a deeper look at hyperplanes, support vectors, kernels, and the optimization principles behind SVMs. To visualize these geometric ideas in action, be sure to watch the full chapter summary above. Supporting Last Minute Lecture helps us continue producing clear, engaging breakdowns of complex machine learning concepts. From Optimal Margin Classifiers to SVM...

Gradient Descent, LMS, and the Mathematics of Error Reduction | Chapter 3 of Why Machines Learn Chapter 3, “The Bottom of the Bowl,” from Why Machines Learn: The Elegant Math Behind Modern AI traces one of the most influential inventions in machine learning history: the Least Mean Squares (LMS) algorithm developed by Bernard Widrow and Ted Hoff. This chapter explores how the LMS rule allowed early artificial neurons to learn from errors through simple, iterative updates—setting the stage for modern optimization techniques like gradient descent and stochastic gradient descent. This post expands on the chapter’s narrative and explains the mathematical intuition behind how machines learn to minimize error. For a more guided walkthrough, be sure to watch the video summary above. Supporting Last Minute Lecture helps us continue creating clear, accessible study tools for students and lifelong learners. The Birth of the LMS Algorithm Widrow and Hoff developed the LMS algorithm w...