Duke University
Explainable AI (XAI) Specialization
Duke University

Explainable AI (XAI) Specialization

Build Ethical and Transparent AI Systems. Master skills in explainability techniques and ethical AI development to create trustworthy and transparent machine learning solutions.

Brinnae Bent, PhD

Instructor: Brinnae Bent, PhD

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Get in-depth knowledge of a subject
Intermediate level

Recommended experience

1 month
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

1 month
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Implement XAI approaches to enhance transparency, trust, robustness, and ethics in decision-making processes.

  • Build interpretable models in Python, including decision trees, regression models, and neural networks.

  • Apply advanced techniques like LIME, SHAP, and explore explainability for LLMs and computer vision models.

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Taught in English
Recently updated!

September 2024

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Specialization - 3 course series

What you'll learn

  • Define key Explainable AI terminology and their relationships to each other

  • Describe commonly used interpretable and explainable approaches and their trade-offs

  • Evaluate considerations for developing XAI systems, including XAI evaluation approach, robustness, privacy, and integration with decision-making

Skills you'll gain

Category: XAI
Category: Machine Learning
Category: Explainable AI (XAI)
Category: Artificial Intelligence
Category: Interpretable Machine Learning

What you'll learn

  • Describe and implement regression and generalized interpretable models

  • Demonstrate knowledge of decision trees, rules, and interpretable neural networks

  • Explain foundational Mechanistic Interpretability concepts, hypotheses, and experiments

Skills you'll gain

Category: Machine Learning
Category: Responsible AI
Category: Artificial Intelligence
Category: Mechanistic Interpretability
Category: Interpretable Machine Learning

What you'll learn

  • Explain and implement model-agnostic explainability methods.

  • Visualize and explain neural network models using SOTA techniques.

  • Describe emerging approaches to explainability in large language models (LLMs) and generative computer vision.

Skills you'll gain

Category: Neural Network Models
Category: Explainable AI (XAI)
Category: Machine Learning
Category: Artificial Intelligence
Category: Interpretable Machine Learning

Instructor

Brinnae Bent, PhD
Duke University
3 Courses146 learners

Offered by

Duke University

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