CONVEX OPTIMIZATION FOR MACHINE LEARNING A Self-Study Course in the Mathematics That Makes Models Train

CONVEX OPTIMIZATION FOR MACHINE LEARNING: A Self-Study Course in the Mathematics That Makes Models Train
English | June 1, 2026 | ASIN: B0GYLW2TBQ | 307 pages | Epub | 5.95 MB
Convex Optimization for Machine Learning is a practical and mathematically rigorous guide designed to help students, engineers, and self-learners understand the optimization principles that power reliable machine learning models.Most machine learning training problems are optimization problems-but not all optimization problems are created equal. Some guarantee dependable solutions, while others hide traps of local minima and unstable convergence. The key difference is convexity.This book provides a clear, step-by-step path from foundational concepts to real machine learning applications, explaining not only how optimization works but why certain models are more reliable than others.Inside this book, you will learn:* What convex sets and convex functions are-and why they matter* The geometry behind optimization and feasible regions* Gradient descent and optimization fundamentals* Subgradients and nonsmooth optimization* Proximal methods and constrained optimization* Duality and Lagrangian methods* Support Vector Machines (SVMs) from an optimization perspective* Regularized empirical risk minimization* Accelerated optimization methods* Stochastic gradient descent (SGD) and scalable learning* The mathematical foundations behind ridge regression, lasso, logistic regression, and related ML modelsDesigned as a self-study guide, the book includes:* Intuitive explanations before formal mathematics* Fully worked examples with no skipped steps* Practice, application, and challenge problems* Computational labs and ML connections throughout* Linear algebra and multivariable calculus refreshers* Solutions to all problemsWhether you are a university student, data scientist, engineer, or independent learner seeking a deeper understanding of machine learning mathematics, this book provides the tools to move beyond black-box training and understand optimization with confidence.From convexity to convergence-learn the mathematics that makes machine learning reliable.