MSE598DM/CSE598DM/ME598DM :: MatSE Illinois :: University of Illinois at Urbana-Champaign


Online discussion forum

This class uses the CampusWire System for announcements, updates, and all communication. Please visit this page to access it.

Excused Absences

Excused absences may be requested by filling out the Excused Absences form. For more information, please read the course syllabus.


Recordings will be posted under this link.

Date Reading Material Topic/Description Instructor/Notes
1/16 Anaconda, Article Introduction, Course Outline, Computer Environment André Schleife
1/18 ML online course, When Not To Use ML Overview: Supervised, Unsupervised, Reinforcement Learning André Schleife
1/23 Youtube Link Data: Data types, data sources, cleanup, preparation, curation André Schleife
1/25 Python Basics, Jupyter Notebook André Schleife
1/30 Python for data science I Andre on travel
Luke Olson
2/1 Python for data science II Andre on travel
Luke Olson
2/6 ML online course, Youtube Link Intro to Materials Science; Linear Regression André Schleife
2/8 Generalized linear models, Bayesian models, Horseshoe Crab Data Andre on travel
Bo Li
2/13 Paper Databases and Band Alignment André Schleife
2/15 Paper Band alignment André Schleife
2/20 Paper Self Driving Labs André Schleife
2/22 Create Account 4Ceed - Motivation and front-end with demos (Guide, Experiment, Slides) Robert Kaufman, Leah Espenhahn (Klara Nahrstedt)
2/27 4ceed Backend - Storage of scientific data Andre on travel
Klara Nahrstedt
2/29 Senselet - Introducing sensing in scientific laboratories (Guide, Slides) Andre on travel
Beitong Tian (Klara Nahrstedt)
3/5 Need for cyber-tools for maintenance of scientific instruments in labs; Hands-on for MAINTTrak tool within the MAINTLET project Andre on travel
Janam Bagdai, Beiton Tian (Klara Nahrstedt)
3/7 Bayesian Optimization Elif Ertekin
3/12 Spring Break
3/14 Spring Break
3/19 Gaussian Process Regression Elif Ertekin
3/21 Defect ID using statistical learning methods Notes Harley Johnson
3/26 Defect ID using statistical learning methods Notes Harley Johnson
3/28 Clustering and Principal Component Analysis André Schleife
4/2 Discussion about automated labs André Schleife
4/4 Image processing for TEM: Handout, RR_2143 STEM 4.6 Mx HAADF_25.1pA_1024px_2.0us_Raw_Stack_16bit_TopRightQuarter.tif, RR_2143 STEM 4.6 Mx HAADF_25.1pA_1024px_2.0us_Raw_Stack_16bit.tif Rahim Raja (Pinshane Huang)
4/9 Image processing for TEM II, Recall and Precision Rahim Raja (Pinshane Huang)
4/11 Why do LLMs work? What are LLMs good for? Fum & materials science digital experts Calvin Li
4/16 Uncertainty quantification Slides/Book Chapter/Exercise Dallas Trinkle
4/18 Uncertainty quantification Dallas Trinkle
4/23 Inference in science Andre on travel
Lucas Wagner
4/25 Inference in science II Andre on travel
Lucas Wagner
4/30 Decision Trees and Random Forests André Schleife

Course Description

This course is a multidisciplinary introduction to topics at the intersection of materials and data science. Corresponding to this, it brings in speakers and activities from 6 different departments (computer science, information science, statistics, physics, materials science, and mechanical engineering) to provide their own perspectives on this subject.


Course Grading


Attendance of each unit will be graded through a GradeScope assignment, filled out in class. Class participation on CampusWire will be graded, and it is expected that you ask two questions throughout the semester and answer two questions throughout the semester. Several of the units will involve hands-on sessions. For these, you will turn in a PDF report on each of your hands on activities. The report should include a narrative about what you are doing and why. Learning how to do this is an important part of doing science. Please consult the course syllabus for details.