MSE598 ML :: 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 Notes/Assignments
1/17 Anaconda Course Outline, Computer Environment, Statistics
1/19 ML online course, When Not To Use ML Overview: Supervised, Unsupervised, Reinforcement Learning
1/24 Youtube Link, Youtube Link, ML online course Deployment, Introduction: Python/Jupyter, numpy
1/26 Youtube Link, Youtube Link, ML online course Random numbers, Hands-on: Linear Algebra and Statistics with Python
1/31 Youtube Link, FAIR Data: Data types, data sources, cleanup, preparation, curation; FAIR principles
2/2 ML online course, ML online course, Youtube Link Hands-on: matplotlib, pandas
2/7 FHI paper, ML online course Models: Linear Regression, Evaluation, Feature Engineering (Descriptors and descriptor selection, LASSO), Tuning, Model selection
2/9 ML online course Hands-on: Regression, Model Selection, Feature Engineering
2/14 ML online course Models: Classification, Training, Evaluation, Generative Techniques
2/16 ML online course Hands-on: Classification, Training, etc.
2/21 Unsupervised Learning: Dimensionality Reduction Via Matrix Decomposition (nanohub) Models: Clustering Techniques, SVM, Principal Component Analysis, Kernel Methods, Kernel Ridge Regression
2/23 Hands-on: Unsupervised Learning
2/28 Repositories and Data Management (book and nanohub) Data: Construction and usage of databases, Repositories and Data Management, APIs
3/2 Hands-on: Databases
3/7 ML online course Models: Trees and Ensemble Learning, Decision Trees, Forests, Boosting, Feature importance Andre on travel
3/9 ML online course Hands-on: Trees, Forests Andre on travel
3/14 Spring Break
3/16 Spring Break
3/21 ML online course Models: Deep learning, Neural Networks
3/23 ML online course Hands-on: Deep learning, Tensorflow
3/28 Papers Data: Atomic Geometries in Materials Science (SOAP, MBTR)
3/30 ICSD (Book), PAULING/AIIDA (Book) Data: Images, ICSD, PAULING/AIIDA, Ontologies
4/4 TBD Image analysis, Microscopy
4/6 TBD Image analysis, Microscopy
4/11 TBD Potentials for Molecular Dynamics
4/13 TBD Potentials for Molecular Dynamics
4/18 TBD Automatic Computation: Materials Project and NOMAD Andre might be on travel
4/20 TBD Descriptors and Electronic Structure Andre might be on travel
4/25 Nanohub Models: Active Learning via Bayesian Optimization
4/27 Nanohub Self-Driving Labs, Machine Learning Predicts Additive Manufacturing Part Quality
5/2 Nanohub Parsimonious Neural Networks Learn Interpretable Physical Laws

Course Description

This course is an in-depth introduction into the use of machine-learning techniques in the field of materials science. Necessary foundations will be introduced, including how to curate and maintain data in databases, how to visualize it, and how to turn it into a descriptor compatible with machine-learning models. Supervised, unsupervised, and reinforcement techniques to machine learning will be introduced, explained, and students will use these techniques themselves on various data sets. Regression, classification, and deep learning models will be introduced and used by students. The course will also explain modern applications of machine learning in the field of materials science using recent research papers. Hands-on tutorials will be in Python.


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 (check the "Notes/Assignments" column above) and it is required that you to submit a jupyter notebook of your activities, which will be graded.