Introduction to Machine Learning Model Explanations and Interpretability

Overview

Explore key concepts in data science explanations, from gradient-based highlighting to contrastive editing, covering essential techniques for understanding and interpreting complex models.

Syllabus

• Course Overview
Introduction to Course Objectives
Importance of Interpretability in Machine Learning
• Foundations of Model Interpretability
Definitions and Terminology
Trade-offs Between Accuracy and Interpretability
Types of Machine Learning Models (Black Box, White Box)
• Gradient-based Explanation Techniques
Saliency Maps
Integrated Gradients
Gradient-weighted Class Activation Mapping (Grad-CAM)
• Local Interpretability Methods
Local Interpretable Model-agnostic Explanations (LIME)
Shapley Additive Explanations (SHAP)
Partial Dependence Plots (PDPs)
• Global Interpretability Methods
Feature Importance
Global Surrogate Models
Feature Interaction Effects
• Contrastive Explanations
Concept of Contrastive Explanations
Techniques for Contrastive Explanation Generation
• Model-Specific Interpretability Techniques
Interpretability in Decision Trees and Rule-Based Models
Interpretability in Neural Networks
Interpretability in Bayesian Models
• Advanced Topics in Interpretability
Counterfactual Explanations
Causal Explanations
Ethical Considerations and Bias in Model Interpretability
• Case Studies and Real-world Applications
Practical Interpretability in Industry Applications
Interactive Explanation Tools
Challenges and Solutions in Model Interpretability
• Hands-on Workshops and Assignments
Using Interpretability Libraries and Tools (e.g., SHAP, LIME, ELI5)
Interpretability in Practice Sessions
Collaborative Group Project
• Conclusion and Future Trends
Current Challenges and Future Directions in Model Interpretability
Recap and Resources for Further Learning

Subjects

Data Science