This course is to teach students modern numerical optimization methods for large scale systems. Topics covered in this course include gradient method, subgradient method, proximal gradient method, Nesterov’s acceleration technique, alternating direction method of multipliers, coordinate descent method, and stochastic / randomized algorithms. Applications of these optimization methods for solving problems in contemporary applications arising from big data analytics, machine learning, statistics, signal processing etc. will also be discussed.