What You Need to Know Before
You Start
Starts 6 June 2025 02:53
Ends 6 June 2025
00
days
00
hours
00
minutes
00
seconds
Introduction to Machine Learning Model Explanations and Interpretability
Explore key concepts in data science explanations, from gradient-based highlighting to contrastive editing, covering essential techniques for understanding and interpreting complex models.
UofU Data Science
via YouTube
UofU Data Science
2463 Courses
1 hour 21 minutes
Optional upgrade avallable
Not Specified
Progress at your own speed
Free Video
Optional upgrade avallable
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
- Foundations of Model Interpretability
- Gradient-based Explanation Techniques
- Local Interpretability Methods
- Global Interpretability Methods
- Contrastive Explanations
- Model-Specific Interpretability Techniques
- Advanced Topics in Interpretability
- Case Studies and Real-world Applications
- Hands-on Workshops and Assignments
- Conclusion and Future Trends
Introduction to Course Objectives
Importance of Interpretability in Machine Learning
Definitions and Terminology
Trade-offs Between Accuracy and Interpretability
Types of Machine Learning Models (Black Box, White Box)
Saliency Maps
Integrated Gradients
Gradient-weighted Class Activation Mapping (Grad-CAM)
Local Interpretable Model-agnostic Explanations (LIME)
Shapley Additive Explanations (SHAP)
Partial Dependence Plots (PDPs)
Feature Importance
Global Surrogate Models
Feature Interaction Effects
Concept of Contrastive Explanations
Techniques for Contrastive Explanation Generation
Interpretability in Decision Trees and Rule-Based Models
Interpretability in Neural Networks
Interpretability in Bayesian Models
Counterfactual Explanations
Causal Explanations
Ethical Considerations and Bias in Model Interpretability
Practical Interpretability in Industry Applications
Interactive Explanation Tools
Challenges and Solutions in Model Interpretability
Using Interpretability Libraries and Tools (e.g., SHAP, LIME, ELI5)
Interpretability in Practice Sessions
Collaborative Group Project
Current Challenges and Future Directions in Model Interpretability
Recap and Resources for Further Learning
Subjects
Data Science