Data-driven discovery is revolutionizing how we model predict and control complex systems.
Now with Python and MATLAB® this textbook trains mathematical scientists and engineers for the
next generation of scientific discovery by offering a broad overview of the growing
intersection of data-driven methods machine learning applied optimization and classical
fields of engineering mathematics and mathematical physics. With a focus on integrating
dynamical systems modeling and control with modern methods in applied machine learning this
text includes methods that were chosen for their relevance simplicity and generality. Topics
range from introductory to research-level material making it accessible to advanced
undergraduate and beginning graduate students from the engineering and physical sciences. The
second edition features new chapters on reinforcement learning and physics-informed machine
learning significant new sections throughout and chapter exercises. Online supplementary
material - including lecture videos per section homeworks data and code in MATLAB® Python
Julia and R - available on databookuw.com.
