Open-Source Lectures

This website makes available lectures for the book by S. L. Brunton and J. N. Kutz, “Data-Driven Science and Engineering” (Cambridge 2019). This textbook is used for courses in data-driven engineering and physics-informed machine learning.

This website makes available lectures for the book by J. N. Kutz, “Data-Driven Modeling and Scientific Computation” (Oxford 2013). This textbook is used for courses in scientific computing as well as data analysis.

This website makes available all lectures for AMATH 301, Beginning Scientific Computing. This course provides an introduction to programming and the MATLAB scripting language. It is intended for engineering and physical sciences majors, providing a broad introduction to the power of numerical methods, scientific computing and MATLAB programming.

This website makes available lectures for the course AMATH 568 by J. N. Kutz which surveys the application of asymptotic and perturbation methods in the context of dynamical systems and boundary value problems, including pattern forming systems.

This website makes available lectures for the course AMATH 584 taught by J. N. Kutz on methods for advanced linear algebra and computing.

This website makes available all lectures for AMATH 563, Inferring Structure of Complex Systems. This course provides an broad introduction to a diversity of data-driven methods and machine learning algorithms that help understand networked dynamical systems. It provides an introduction to the power of the numerical algorithms for model discovery using MATLAB programming.

This website makes available a variety of lectures for the emerging method of dynamic mode decomposition. It includes numerous MATLAB codes as well.

This website makes available a variety of lectures for a new course focusing on data methods used in the biological sciences. It includes numerous MATLAB codes as well as lecture materials and lightboard videos.

This course provides a survey of numerical solution techniques for ordinary and partial differential equations. Emphasis is on the application of numerical schemes to practical problems in the engineering and physical sciences.

Exploratory and objective data analysis methods applied to the physical, engineering and biological sciences. Brief overview of statistical methods and their computational implementation for time series analysis, spectral analysis, filtering methods, principal component analysis, proper orthogonal decompositions, and image processing and compression.

Over the Spring and Summer of 2015, I built (with critical help from Derek Franz and Steven Brunton) a lightboard in the Department of Applied Mathematics. The lightboard is a remarkable tool for online education since you can integrate multiple teaching modalities: standard lecturing, powerpoint, MATLAB, and computer drawing programs. This allows the instructor to face the audience, which improves the online engagement over standard methods.

We used this method to film and produce our Data Science for Biologists course which is available on EdX and YouTube.