
E1 260 Optimization for Machine Learning and Data Science (OptML)About E1 260 OptMLThe main goal of E1 260 course is cover optimization techniques suitable for problems that frequently appear in the areas of data science, machine learning, communications, and signal processing. This course focusses on the computational, algorithmic, and implementation aspects of such optimization techniques. This is 3:1 credit course. PrerequisiteBasic linear algebra, probability, and knowledge of Python to conduct simulation exercises. Lectures
SyllabusMathematical background, theory of convex functions, gradient methods, accelerated gradient methods, proximal gradient descent, mirror descent, sub gradient methods, stochastic gradient descent and variants, Project gradient descent and FrankWolfe, alternating direction method of multipliers, nonconvex and submodular optimization. Textbooks
Course requirements and grading
Schedule (pdf)
