Last Minute Lecture

Bayesian Reasoning, Probability Theory, and How Machines Learn from Uncertainty | Chapter 4 of Why Machines Learn Chapter 4, “In All Probability,” from Why Machines Learn: The Elegant Math Behind Modern AI explores the statistical principles that allow machines to navigate uncertainty and make informed predictions. Through famous puzzles like the Monty Hall problem, real-world examples like penguin classification, and foundational probability theory, Anil Ananthaswamy demonstrates how modern AI systems rely on mathematical reasoning under uncertainty. This post expands on the chapter’s most important ideas, focusing on Bayesian thinking, probability distributions, and the inference strategies that power machine learning models. To deepen your understanding of these probabilistic concepts, be sure to watch the chapter summary above. Supporting Last Minute Lecture helps us continue creating accessible, high-quality study resources for learners around the world. Why Probabilit...

Vectors, Dot Products, and the Mathematics Behind Machine Learning | Chapter 2 of Why Machines Learn Chapter 2, “We Are All Just Numbers Here…,” from Why Machines Learn: The Elegant Math Behind Modern AI dives into the mathematical foundations that make learning algorithms possible. Moving from 19th-century discoveries in vector algebra to the modern perceptron, the chapter explains why linear algebra is the language of machine learning. This post expands on the video’s core ideas and provides an accessible walkthrough of the geometry, notation, and logic that help machines interpret the world as numbers. For a deeper guided explanation, be sure to watch the chapter summary above. Supporting the Last Minute Lecture channel helps us keep producing accessible academic breakdowns for complex textbooks. From Quaternions to Vectors: The Birth of Modern AI Mathematics Anil Ananthaswamy begins by tracing the story of William Rowan Hamilton, whose work on quaternions introduced c...