This course integrates decision and data analytics to solve real-world problems. Topics include regression, regularization, dimension reduction, tree-based method, multi-objective optimization, nonlinear optimization, neural networks, etc.
This course focuses on regression and forecasting models and their applications in various fields of science and engineering. Topics include multiple linear regression, diagnostics, and variable selection, time series analysis, dynamical model and recurrent neural networks.
This course brings together machine learning, system informatics and control, and applied mathematics. The objective of this course is to introduce some recent advances, including methodologies and applications, for integrating physics, domain-knowledge into data-driven statistical learning models. In particular, the course focuses on the modeling of data arising from engineering and scientific processes governed by Partial Differential Equations (PDEs). Topics include: PDE-based statistical modeling, convolution models for spatio-temporal processes, reduced-order models, inverse models, data sampling, Proper Orthogonal Decomposition, Dynamic Mode Decomposition, etc. Students will learn some recent advances in data-driven methods that can be applied to a diverse range of complex dynamical systems for modeling, prediction, monitoring and control problems.
This course introduces students to the concepts and methods of data analysis using publicly available and industry-adopted software systems. Students are expected to formulate statistical models, process necessary data, analyze it, draw conclusions, and present solutions.