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.
Any Ideas?
If you have any ideas or feedback about how this workshop could be better structured, let us know!
You can reach the corresponding author of this workshop at:
colin_burdine1@baylor.edu
Tentative Workshop Schedule:#
Session |
Date |
Content |
---|---|---|
Day 0 |
06/16/2023 (2:30-3:30 PM) |
Introduction, Setting up your Python Notebook |
Day 1 |
06/19/2023 (2:30-3:30 PM) |
Python Data Types |
Day 2 |
06/20/2023 (2:30-3:30 PM) |
Python Functions and Classes |
Day 3 |
06/21/2023 (2:30-3:30 PM) |
Scientific Computing with Numpy and Scipy |
Day 4 |
06/22/2023 (2:30-3:30 PM) |
Data Manipulation and Visualization |
Day 5 |
06/23/2023 (2:30-3:30 PM) |
Materials Science Packages |
Day 6 |
06/26/2023 (2:30-3:30 PM) |
Introduction to ML, Supervised Learning |
Day 7 |
06/27/2023 (2:30-3:30 PM) |
Advanced Regression Models |
Day 8 |
06/28/2023 (2:30-3:30 PM) |
Unsupervised Learning |
Day 9 |
06/29/2023 (2:30-3:30 PM) |
Neural Networks |
Day 10 |
06/30/2023 (2:30-3:30 PM) |
Advanced Applications in Materials Science |
Video Recordings of Workshop Sessions:#
Video recordings of the sessions 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
Workshop Session Slides#
You can access the slides for the workshop below:
Day 1: Python Data Types
Day 2: Python Functions and Classes
Day 5: Materials Science Packages
Day 7: Advanced Regression Models
Day 8: Unsupervised Learning
Day 9: Neural Networks
Table of Contents#
- Introduction
- Getting Started with Python
- Python Data Types
- Python Functions and Classes
- Scientific Computing with Numpy and Scipy
- Data Handling and Visualization
- Materials Science Python Packages
- Introduction to Machine Learning
- Supervised Learning
- Advanced Regression Models
- Unsupervised Learning
- Neural Networks
- Applications of ML to Materials Science