As Artificial Intelligence (AI) becomes integrated into high-risk domains like healthcare, finance, and criminal justice, it is critical that those responsible for building these systems think outside the black box and develop systems that are not only accurate, but also transparent and trustworthy. This course is a comprehensive, hands-on guide to Explainable Machine Learning (XAI), empowering you to develop AI solutions that are aligned with responsible AI principles.
Recommended experience
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
Details to know
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September 2024
4 assignments
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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
Offered by
Recommended if you're interested in Machine Learning
Duke University
Duke University
Duke University
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