Optimization for Data Engineering. 3 credits, 3 contact hours

Catalog description

With the emergence of advanced sensing and monitoring technologies, data has become the new oil of our digital economy. In all the engineering fields, ranging from power systems and transportation systems to online shopping, web servers, sensors and cameras are continuously collecting huge amounts of data, but this data will not have any meaning without proper processing, analysis, and learning. Optimization techniques provide the core foundation for the processing of the data and learning from it. This course covers the basic analytical and algorithmic optimization tools that lay the foundation for the data analytics and deep learning systems. Specific topics include single-objective convex optimization (duality, optimality conditions, algorithms), gradients-based methods, and the fundamentals of multi-objective optimization and game theory. The theoretical foundation and the fundamental algorithms are applied to real-world scenarios that utilize supervised learning models, including nonlinear regression, logistic regression, support vector machines, and deep neural networks.