TDA + Neuro

In recent years, ideas from topological data analysis (TDA) have been increasingly adopted in order to analyze real-world data from many fields, including neuroscientific data. In this course, we will cover the fundamental ideas of TDA from the gound-up, assuming no prior knowledge of TDA. We will cover the fundamental theory of TDA on the theoretical level, but with added emphasis on working with TDA in practice via available algorithms and software. We will examine some recent literature where TDA has been applied successfully to give new insights in neuroscience. By the end of the semester, students will be able to apply concepts from TDA to analyze real-world data, and will have completed a multi-stage project in which they go through a full data analysis pipeline using TDA techniques.

More in-depth details on course topics can be found in the syllabus. Communication outside of class time will function through the course gmail account, and the course Slack workspace. Exercises will be assigned via email and Slack, and updated in the Schedule section below, as well as in the Exercises folder.

We gratefully acknowledge NSF grants NSF-IIS-1901360 and NSF-CCF-1839358 for providing funding for this course.