Explainable AI
Learn how to design AI systems that can provide understandable and interpretable explanations for their decisions and outputs, making them more transparent and trustworthy.
Learn how to design AI systems that can provide understandable and interpretable explanations for their decisions and outputs, making them more transparent and trustworthy.
This module covers the basics of explainable AI, including the importance of transparency and interpretability, and different methods for achieving explainability.
This module covers techniques for designing interpretable machine learning models, such as decision trees, rule-based systems, and linear models. Students will learn how to interpret model outputs and feature importance.
This module covers post-hoc explanation methods that can be applied to any machine learning model, such as LIME and SHAP. Students will learn how to generate explanations for model predictions using these techniques.
This module covers the importance of human-centered design in creating explainable AI systems, including user studies and design principles. Students will learn how to evaluate the effectiveness of their explainability methods with human subjects.
Bite-sized daily lessons that you can easily fit into your schedule. Each day, we release new lessons no longer than 15 minutes. Our lessons are carefully curated to ensure that they're both engaging and informative, allowing you to learn something new every day, and at your own pace.
Collaborate with other engineers from around the world, providing you with a unique opportunity to learn from others and build your professional network.
Our live learning sessions are designed to be interactive and engaging, giving you the opportunity to ask questions and interact with subject-matter experts.
Learn by solving real-world problems. Our courses are designed to get rid of the fluff and provide you with the most relevant information to help you apply your learning.
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