Applied Optimization for Wireless, Machine Learning, and Big Data

Mathematical optimization is the hidden engine powering today's most advanced technologies, from training deep neural networks to allocating resources in high-speed wireless networks. Understanding how to formulate and solve these optimization problems is essential for building efficient, scalable systems. This text-based course guides you from foundational mathematical concepts to practical applications in engineering and data science. You will learn how to translate complex real-world challenges into solvable mathematical models. What you'll learn: • Understand the core concepts of convex optimization, objective functions, and constraints. • Apply gradient descent and modern adaptive algorithms like Adam to optimize machine learning models. • Formulate optimization problems for wireless communications, including power allocation and signal processing. • Analyze large-scale data challenges using distributed optimization and regularization techniques. • Solve optimization models step-by-step using clear, structured mathematical workflows. The course begins with essential terminology, basic definitions, and the geometry of optimization before progressing to practical formulations in wireless systems, machine learning, and big data. This course is designed for beginners, engineering students, and data enthusiasts. A basic understanding of linear algebra and calculus is helpful, but no prior experience with optimization theory is required. Start reading today to master the mathematical foundations of modern intelligent systems.