MSE 598

Spring 2025 All Classes

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Credit: 1 TO 4 hours.

Subject offerings of new and developing areas of knowledge in materials science and engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites.

Approved for Letter and S/U grading. May be repeated in the same or separate terms if topics vary.

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MSE 598 class schedule data for spring 2025
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
70522	Lecture-Discussion	DM	11:00AM -12:20PM	TR	2051 Sidney Lu Mech Engr Bldg	Trinkle, D	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 4 hours Section Title: Intro to Digital Materials Section Info: Meets with ME 598 (48380) and CSE 498 (70543). We introduce the connection of materials and data science, and specific issues regarding experimental and computational materials data. Topics include data acquisition and management, data curation, uncertainty quantification, and applying machine learning to materials data. The focus will be on current scientific literature in the emerging materials and data area. Restriction(s): Restricted to Graduate - Urbana-Champaign.
43479	Lecture-Discussion	ML	12:30PM -1:50PM	TR	4101 Materials Science & Eng Bld	Schleife, A	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 4 hours Section Title: Machine Learning for MatSE Section Info: Topics include Python and Jupyter, Statistics Introduction (Splits, Validation/Verification/Testing), Data and Data Curation, Databases and FAIR principles, Descriptors and descriptor selection, Machine Learning Approaches (Decision Trees, Random Forests, Neural Networks, Active Learning, ...), and Ontologies, with examples from current literature in Materials Science and Engineering. Restriction(s): Restricted to Graduate - Urbana-Champaign.
75161	Online	MLO	ARRANGED	n.a.	n.a.	Schleife, A	Part of Term: 1 Date Range: 01/21/25-05/07/25 Special Approval: Instructor Approval Required Credit: 4 hours Section Title: Machine Learning for MatSE Section Info: Topics include Python and Jupyter, Statistics Introduction (Splits, Validation/Verification/Testing), Data and Data Curation, Databases and FAIR principles, Descriptors and descriptor selection, Machine Learning Approaches (Decision Trees, Random Forests, Neural Networks, Active Learning, ...), and Ontologies, with examples from current literature in Materials Science and Engineering. Restriction(s): Restricted to NDEG:Engineering UG ONL - UIUC or NDEG:Engineering GR ONL - UIUC.