MSE598DMO/CSE598DM/ME598DM :: MatSE Illinois :: University of Illinois at Urbana-Champaign
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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.
Schedule
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 travelLuke Olson | |
2/1 | Python for data science II | Andre on travelLuke 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 travelBo 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 travelKlara Nahrstedt | |
2/29 | Senselet - Introducing sensing in scientific laboratories (Guide, Slides) | Andre on travelBeitong 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 travelJanam 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 travelLucas Wagner | |
4/25 | Inference in science II | Andre on travelLucas 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.
Scope
- Introduction to the connection of materials and data science
- Specific issues regarding experimental and computational materials data
- Data acquisition and management, data curation
- Uncertainty quantification
- Applying machine learning to materials data
Course Grading
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.