MSE598DM/CSE598DM/ME598DM :: MatSE Illinois :: University of Illinois at Urbana-Champaign
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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.
Week | Tuesday | Thursday | Topic | Instructor |
---|---|---|---|---|
1 | 1/18 | 1/20 | Introduction, python basics, python notebook | André Schleife |
2 | 1/25 | 1/27 | Python for data science I, II | Luke Olson |
3 | 2/1 | 2/3 | Visualization Hands-on activity | Matthew Turk |
4 | 2/8 | 2/10 | Intro to material science for non-experts I, II | André Schleife |
5 | 2/15 | 2/17 | Generalized linear models (PDF), Bayesian models, Horseshoe Crab Data | Bo Li |
6 | 2/22 | 2/24 | Band alignment I, II | André Schleife |
7 | 3/1 | 3/3 | Defect ID using statistical learning methods Notes, Article | Harley Johnson |
8 | 3/8 | 3/10 | 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 | Pinshane Huang |
9 | 3/15 | 3/17 | Spring Break | |
10 | 3/22 | 3/24 | 4CeeD Handout I, 4CeeD Handout II, 4CeeD Notebook, 4CeeD Notebook PDF (Bracelet, Senselet) | Klara Nahrstedt |
11 | 3/29 | 3/31 | Senselet Guide, Backend, Slides | Klara Nahrstedt |
12 | 4/5 | 4/7 | Model development from first principles, The sometimes surprising behavior of magnetic spins on a complex surface: Visualizing Orbitals and managing data | Lucas Wagner, Barbara Jones (IBM) |
13 | 4/12 | 4/14 | Uncertainty quantification Slides/Book Chapter/Exercise | Dallas Trinkle |
14 | 4/19 | 4/21 | Accelerated discovery in chemistry: representation learning and recommender system (Paper I, Paper II, Slides), Semi-autonomous) experimental systems: Paper/Slides 1/Slides II | Dmitry Zubarev (IBM), Elif Ertekin |
15 | 4/26 | 4/28 | Graphene ResQ (T) Neural network/(H)Bayesian search, Slides, Gaussian Process Slides | Elif Ertekin |
16 | 5/3 | – | Q and A | Johnson, Schleife, Huang, Turk, Li, and others |
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
Each unit, noted below in the schedule, will involve hands-on sessions. 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.