Why Machines Learn — Complete Chapter Index & Video Playlist

Welcome to the complete chapter index for Why Machines Learn: The Elegant Math Behind Modern AI by Anil Ananthaswamy — your hub for chapter-by-chapter summaries, mathematical insights, and quick access to every video in the series.

Book cover

Use this page to navigate the full progression of machine learning — from early pattern recognition and probability theory to support vector machines, convolutional networks, and deep generative models.

Chapters & Breadcrumb Schema

Explore the Full Video Playlist

📚 Prefer to watch everything in order? View the complete YouTube playlist:
Why Machines Learn — Complete Video Lecture Series

All Chapter Links

Chapter 1 — Pattern Recognition and the Birth of Machine Learning Explained
Chapter 2 — Vectors, Dot Products, and the Mathematics Behind Machine Learning
Chapter 3 — Gradient Descent, LMS, and the Mathematics of Error Reduction
Chapter 4 — Bayesian Reasoning, Probability Theory, and How Machines Learn from Uncertainty
Chapter 5 — Nearest Neighbors, Distance Metrics, and Pattern Recognition Explained
Chapter 6 — PCA, Eigenvectors, and the Hidden Structure of High-Dimensional Data
Chapter 7 — Support Vector Machines, Kernel Methods, and Nonlinear Classification Explained
Chapter 8 — Hopfield Networks, Energy Minimization, and Associative Memory Explained
Chapter 9 — The Universal Approximation Theorem and the Debate Over Deep vs. Shallow Networks
Chapter 10 — Backpropagation, Gradient Descent, and the Rise of Deep Learning
Chapter 11 — How Neuroscience Shaped Convolutional Neural Networks
Chapter 12 — Restricted Boltzmann Machines, Deep Belief Networks, and the Mathematics of Artificial Dreaming

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