MSE598 ML :: 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 | 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.
Scope
- Statistics in Python
- Machine learning models and their application to data
- Data sources, curation, management, and visualization
- Applications from recent research literature
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 (check the "Notes/Assignments" column above) and it is required that you to submit a jupyter notebook of your activities, which will be graded.