About Workshop
Interpretable ML for Scientific Discovery is a 3-day online hands-on workshop designed to help participants build, evaluate, and interpret machine learning models for scientific data analysis.
The workshop focuses on using Scikit-learn, SHAP, and Yellowbrick to understand model performance, identify influential variables, visualize diagnostics, and extract meaningful scientific insights from machine learning predictions.
Aim
The aim of this workshop is to help participants develop practical skills in interpretable machine learning for scientific discovery. Participants will learn how to build ML models, evaluate their reliability, explain model behavior, and connect model outputs with scientific reasoning.
What Participants Will Learn
- Understand the role of machine learning in scientific discovery.
- Frame scientific datasets as ML problems.
- Build baseline models using Scikit-learn.
- Evaluate model performance and reliability.
- Understand feature importance and permutation importance.
- Use Yellowbrick for model diagnostics and visualization.
- Apply SHAP for global and local model explanations.
- Extract scientific insights from interpretable ML results.
Structure
📅 Day 1: Machine Learning Foundations for Scientific Data Analysis
Role of machine learning in scientific discovery Understanding scientific datasets, variables, features, and labels Framing scientific problems as ML tasks Data preprocessing and train-test split workflow Building baseline models using Scikit-learn Model evaluation and reliability interpretation Understanding how ML results support scientific decision-making 🛠️ Hands-on: Hands-on 1: Load and prepare a scientific-style dataset using Scikit-learn Hands-on 2: Build and evaluate a baseline ML model using Scikit-learn 🧰 Tools Covered: Scikit-learn, Python, Jupyter Notebook / Google Colab📅 Day 2: Feature Importance and Visual Model Interpretation
Introduction to interpretable machine learning Understanding feature importance in scientific models Built-in feature importance vs permutation importance Using permutation importance to identify influential variables Yellowbrick visualizations for model diagnostics Interpreting influential variables in scientific datasets Using visualization to support model reliability and interpretation 🛠️ Hands-on: Hands-on 1: Perform feature importance analysis using Scikit-learn Hands-on 2: Apply permutation importance and create Yellowbrick visualizations 🧰 Tools Covered: Scikit-learn, Yellowbrick, Python, Jupyter Notebook / Google Colab📅 Day 3: SHAP Explainability and Scientific Insight Extraction
Introduction to SHAP for explainable AI Local vs global model interpretation SHAP summary plots for global explanations SHAP explanations for individual predictions Connecting model explanations with scientific reasoning Guided final exercise for scientific insight extraction Preparing a short scientific interpretation from model explanations 🛠️ Hands-on: Hands-on 1: Generate SHAP explanations for global model interpretation Hands-on 2: Interpret individual predictions and prepare a short scientific insight summary 🧰 Tools Covered: SHAP, Scikit-learn, Yellowbrick, Python, Jupyter Notebook / Google ColabImportant Dates
Registration Ends
4.30 pm
Workshop Dates
2026-06-02
5.30 PM
5.30 PM
What You Will Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Outcomes
- Prepare scientific-style datasets for ML workflows.
- Build baseline machine learning models using Scikit-learn.
- Evaluate ML models for reliability and performance.
- Interpret feature importance in scientific datasets.
- Apply permutation importance for robust model interpretation.
- Use Yellowbrick visualizations for model diagnostics.
- Generate SHAP explanations for global and local model interpretation.
- Connect ML explanations with scientific reasoning.
- Prepare a short scientific insight summary based on model results.
Who Should Attend
- PhD scholars and research scholars working with scientific datasets
- Postgraduate students interested in machine learning for research
- Academicians and faculty members exploring interpretable AI
- Researchers from science, engineering, biotechnology, healthcare, environment, or materials domains
- Industry R&D professionals using data-driven models
- Data science beginners interested in explainable AI
- Participants who want to understand how ML models make predictions
Deliverables
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience