Materials + ML Workshop#
Welcome to the Materials + ML Online Book!
The Materials + ML Workshop (ML is short for Machine Learning) is a beginner-friendly workshop focused on developing basic skills in scientific Python programming, data analysis, and machine learning with a focus on how these skills can be applied to real-world research problems in materials science.
This online book contains the material that will be covered each day of the workshop. It is encouraged that you read the content for each day of the workshop prior to attending, so that you can ask questions related to what is covered each day.
Feedback
If you have any ideas or feedback regarding this workshop, let us know!
You can reach the corresponding author of this workshop at:
colin_burdine1@baylor.edu
2025 Workshop Schedule:#
Session |
Date |
Content |
|---|---|---|
Week 1 |
||
Day 1 |
06/09/2025 (2:00-4:00 PM) |
Introduction, Python Data Types |
Day 2 |
06/10/2025 (2:00-4:00 PM) |
Python Functions and Classes |
Day 3 |
06/11/2025 (2:00-4:00 PM) |
Scientific Computing with Numpy and Scipy |
Day 4 |
06/12/2025 (2:00-4:00 PM) |
Data Manipulation and Visualization |
Day 5 |
06/13/2025 (2:00-4:00 PM) |
Materials Science Packages, Introduction to ML |
Week 2 |
||
Day 6 |
06/16/2025 (2:00-4:00 PM) |
Introduction to ML, Supervised Learning |
Day 7 |
06/17/2025 (2:00-4:00 PM) |
Advanced Regression Models |
Day 8 |
06/18/2025 (2:00-5:00 PM) |
Unsupervised Learning |
|
|
|
Day 10 |
06/20/2025 (2:00-5:00 PM) |
Neural Networks, Advanced Applications |
Workshop Session Slides#
You can access the slides from the workshop as they become available here:
Day 2: Python Functions and Classes
Day 5: Materials Science Packages
Day 7: Advanced Regression Models
Day 8: Unsupervised Learning
Day 9: Neural Networks
Workshop Recordings#
Recordings of the 2025 sessions are available on the Workshop’s YouTube playlist.
Previous Materials + ML Workshops:#
This workshop was previously held in Summer 2023
Video recordings of sessions from this workshop can be found on mediaspace.baylor.edu. Accessing these recordings requires a Baylor email address. If you are having trouble accessing the recordings, please send an email to colin_burdine1@baylor.edu.
Table of Contents#
- Introduction
- Getting Started with Python
- Python Data Types
- Python Functions and Classes
- Scientific Computing with Numpy and Scipy
- Data Analysis and Visualization
- Materials Science Python Packages
- Introduction to Machine Learning
- Supervised Learning
- Advanced Regression Models
- Unsupervised Learning
- Neural Networks