Optimization for Machine Learning

Description

The aim of this course is to introduce to students the theory and algorithms for optimization problems that arise in machine learning and data science. In particular, complexity, robustness and scalability of algorithms to large datasets will be discussed in theory and in implementation. By the end of the course the student will: be able to formulate machine learning tasks as optimization problems, be able to tell which optimization formulation is more suitable for the machine learning task at hand, based on complexity, scalability, convexity and smoothness aspects, have a profound understanding of a wide variety of optimization algorithms and their properties, and will be able to apply the appropriate algorithms for a given machine learning task, be able to implement optimization algorithms for large-scale machine learning problems.