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Starts 27 June 2025 11:29

Ends 27 June 2025

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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

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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