This repository includes the source code for the course "ME 539 Introduction to Scientific Machine Learning," which is being taught during Fall 2024 by Dr. Alex Alberts at Purdue University. The in-person version of the course meets every Tuesday and Thursday 4:30-5:45 pm at HAMP 2118. Please only come to the room if registered, as there is insufficient physical space. There is also a Purdue online version of the class and an EdX version.
There are pre-recorded videos of all lectures. These are accessible to Purdue students through Brightspace. EdX students will find the same videos in EdX. More details about the syllabus and course evaluations are available on the corresponding websites.
The material is under the GNU General Public License. You can reuse it in any way you like as soon as you include the same License and cite this repository. Please email me (ibilion@purdue.edu) if you do, as I would love to know!
You can find the lecture book here.
This course evolved from the ME 597 "Data Analytics for Scientists and Engineers," taught two times by Prof. Bilionis, and the ME 597/MA 598 "Introduction to Uncertainty Quantification," taught three times by Prof. Bilionis (the first time, Spring 2016 it was co-taught with Prof. Guang Lin). If you are interested in accessing the old versions of the course, they can be found here.
Note that there is also a 1-credit undergraduate version of the course under ME 297, "Introduction to Data Science for Mechanical Engineers." This version can be found here.