Interpretable Machine Learning Courses and Certifications

Institute for Advanced Study

Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead - Cynthia Rudin

Advocating for interpretable machine learning models in high-stakes decisions, highlighting their reliability and transparency compared to black box models with potentially misleading explanations.

• YouTube
• 1 hour 4 minutes
• On-Demand
• Free Video

Institute for Advanced Study

Introduction to Interpretable Machine Learning II - Cynthia Rudin

Explore interpretable machine learning with a focus on decision trees, information theory, and analytical bounds. Gain insights into modern approaches and practical applications.

• YouTube
• 1 hour 5 minutes
• On-Demand
• Free Video

Institute for Advanced Study

Introduction to Interpretable Machine Learning - Cynthia Rudin

Explore interpretable machine learning with Cynthia Rudin, covering classification, natural language processing, and decision trees. Gain insights into model complexity and information theory applications.

• YouTube
• 57 minutes
• On-Demand
• Free Video

1littlecoder

Interpretable Machine Learning - Kaggle Course

Discover machine learning explainability through feature importance, SHAP values, and partial dependence plots with hands-on Python implementation and real-world use cases.

• YouTube
• 2 hours 5 minutes
• On-Demand
• Free Video

1littlecoder

SHAP and Shapley Values for Interpretable Machine Learning - Kaggle 30 Days of ML Day 18

Explore SHAP and Shapley Values for interpretable machine learning. Learn local interpretation techniques, Python implementation, and visualization methods for explainable AI.

• YouTube
• 21 minutes
• On-Demand
• Free Video

Chemometrics & Machine Learning in Copenhagen

Interpretable Machine Learning with an Eye for the Physics

Explore hybrid multivariate modeling combining theory-driven preprocessing, data-driven chemometrics, and machine learning to extract meaningful information from complex big data measurements.

• YouTube
• 59 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Interpretable Machine Learning via Program Synthesis - IPAM at UCLA

Explore interpretable machine learning through program synthesis, focusing on control policies and RNA splice prediction. Learn novel approaches for creating custom, problem-specific model families.

• YouTube
• 47 minutes
• On-Demand
• Free Video

Alan Turing Institute

Scoring Systems - At the Extreme of Interpretable Machine Learning - Cynthia Rudin - Duke University

Explore interpretable machine learning for high-stakes decisions. Learn about optimal scoring systems, their applications in healthcare and criminal justice, and the importance of transparency in AI models.

• YouTube
• 46 minutes
• On-Demand
• Free Video

Broad Institute

Interpretable Machine Learning for Model Oversight and Insight

Explore interpretable machine learning techniques for model oversight and gaining valuable insights with Finale Doshi-Velez in this colloquium on AI applications in biomedicine.

• YouTube
• 55 minutes
• On-Demand
• Free Video

Centre de recherches mathématiques - CRM

Inference for Interpretable Machine Learning: Feature Importance and Beyond

Explore statistical inference frameworks for quantifying uncertainty in machine learning interpretations, focusing on feature importance and patterns to promote trust and transparency in ML systems.

• YouTube
• 1 hour 2 minutes
• On-Demand
• Free Video

NashKnolX

Interpretable Machine Learning: Understanding Black-Box Models with SHAP and LIME

Discover essential techniques for making complex ML models transparent, from SHAP to LIME, and learn to explain predictions, detect biases, and build trustworthy AI systems through practical demonstrations.

• YouTube
• 41 minutes
• On-Demand
• Free Video

Coursera

Interpretable Machine Learning Applications: Part 1

81 ratings at Coursera

Learn to create interpretable machine learning applications using decision trees and random forests. Gain insights into feature importance and model behavior for more accountable and trusted ML applications.

• Coursera
• 1 hour 40 minutes
• On-Demand
• Paid Course

Coursera

Interpretable Machine Learning Applications: Part 2

27 ratings at Coursera

Develop interpretable machine learning applications using LIME to explain individual predictions, enhancing model transparency and accountability for data scientists and decision-makers.

• Coursera
• 1 hour 55 minutes
• On-Demand
• Paid Course

Coursera

Interpretable machine learning applications: Part 3

25 ratings at Coursera

Learn to generate explanations for AI predictions using similar cases, enhancing model interpretability and decision-making in business contexts like loan approvals.

• Coursera
• 1 hour 55 minutes
• On-Demand
• Paid Course

Coursera

Interpretable Machine Learning Applications: Part 4

19 ratings at Coursera

Explore interpretable machine learning using Google's What-If Tool. Learn to analyze and visualize prediction model behavior for white wine quality classification using Decision Trees and Random Forests.

• Coursera
• 1 hour 55 minutes
• On-Demand
• Paid Course