Duke University
Explainable Machine Learning (XAI)
Duke University

Explainable Machine Learning (XAI)

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

14 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

14 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace

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.

Details to know

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Recently updated!

September 2024

Assessments

4 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the Explainable AI (XAI) Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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There are 3 modules in this course

In this module, you will be introduced to the concept of model-agnostic explainability and will explore techniques and approaches for local and global explanations. You will learn how to explain and implement local explainability techniques LIME, SHAP, and ICE plots, global explainable techniques including functional decomposition, PDP, and ALE plots, and example-based explanations in Python. You will apply these learnings through discussions, guided programming labs, and a quiz assessment.

What's included

19 videos6 readings1 assignment4 discussion prompts3 ungraded labs

In this module, you will be introduced to the concept of explainable deep learning and will explore techniques and approaches for explaining neural networks. You will learn how to explain and implement neural network visualization techniques, demonstrate knowledge of activation vectors in Python, and recognize and critique interpretable attention and saliency methods. You will apply these learnings through discussions, guided programming labs and case studies, and a quiz assessment.

What's included

8 videos5 readings2 assignments1 discussion prompt2 ungraded labs

In this module, you will be introduced to the concept of explainable generative AI. You will learn how to explain emerging approaches to explainability in LLMs, generative computer vision, and multimodal models. You will apply these learnings through discussions, guided programming labs, and a quiz assessment.

What's included

7 videos3 readings1 assignment2 discussion prompts2 ungraded labs

Instructor

Brinnae Bent, PhD
Duke University
3 Courses168 learners

Offered by

Duke University

Recommended if you're interested in Machine Learning

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