Optimization Algorithms

Catalog description

This course covers methods for optimization, the process of finding the optimal set of parameters for a given objective function, which is a problem at the core of many applications. The course will cover combinatorial optimization, where parameters consist of discrete values; numerical optimization, where parameters consist of continuous values; and hybrid methods, where parameters can be both discrete and continuous. After these methods have been introduced, the course covers how they can be applied to optimization of artificial intelligence models through AutoML methods such as hyperparameter optimization and neural architecture search.