ML Bootcamp

ML Bootcamp

This page is dedicated to the ML Bootcamp track.

Program Overview

• Foundations of machine learning for chemistry and data-driven science
• Supervised learning workflows from data curation to model validation
• Feature engineering and model interpretation for experimental datasets
• Reproducible notebooks and project-based practice

Modules

1. Python and scientific stack refresh (NumPy, pandas, matplotlib)
2. Data preparation and exploratory analysis
3. Regression and classification fundamentals
4. Model evaluation, cross-validation, and error analysis
5. Intro to neural networks and practical deployment tips

Practice and Resources

• Hands-on exercises and mini-projects will be published here.
• Lecture notes, datasets, and notebooks can be added under /assets/ml_bootcamp/.

Coming Soon

Detailed schedule, assignment briefs, and links to downloadable materials.