Course Description:

This course deals with the problem of identifying and building mathematical and computational models directly from data. The techniques covered in the course apply to a wide range of systems, including biological systems, electro-mechanical systems, economic systems; this will be reflected in the course assignments. Specific topics that will be addressed in the course include an overview of systems and model types, such as state-space models and distributed parameter models; an in-depth discussion of parametric estimation methods, such as regression and least-squares methods; recent subspace identification methods; data preprocessing techniques; and a discussion of model validation methods. Note that this course is intended primarily for graduate students in engineering who have some familiarity with dynamical systems and stochastic signals.

Recommended Prerequisites

Syllabus

Text

Take-home Final    (Due Dec. 16 at Noon)

Homeworks & Solutions

Projects

Teaching Assistant