Interpretable Machine Learning for Scientific Research and Discovery
Make Machine Learning Models Transparent, Reliable, and Scientifically Meaningful.
Skills you will gain:
About Program:
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.
Program Objectives:
- 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.
What you will learn?
📅 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 Colab
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- 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
Basic knowledge of Python or machine learning is helpful, but advanced expertise is not mandatory.
Career Supporting Skills
Program 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.