Home Courses AI skills for engineers: Data creation and collection Course materials Lectures 2.4.1 Interpreting and Evaluating ML Models: Part I

2.4.1 Interpreting and Evaluating ML Models: Part I

Course subject(s) Module 2. Crowdsourcing for AI

In these two lectures, we introduce the notion of model interpretability and the role of humans in such a process. Furthermore, we discuss what an explanation is in this context as well as how existing model interpretability methods, like LIME, work.

Interpreting and Evaluating ML Models Part I

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AI skills for engineers: Data creation and collection by TU Delft OpenCourseWare is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Based on a work at https://online-learning.tudelft.nl/courses/data-creation-and-collection-for-artificial-intelligence-via-crowdsourcing/ /
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