muandet-lab/ipml-course
A course on imprecise probabilistic machine learning
This course teaches you how to build more robust and trustworthy machine learning models by better accounting for uncertainty. You'll learn the theory of imprecise probability and how to apply it, moving beyond standard probability measures to handle complex, real-world ambiguities. It's designed for machine learning practitioners, researchers, and anyone developing AI systems who needs to improve model safety and reliability.
134 stars.
Use this if your current machine learning models struggle with multifaceted uncertainties in real-world data, leading to issues in trustworthiness, safety, or robustness.
Not ideal if you are looking for a quick, plug-and-play solution without diving into foundational theory and hands-on implementation.
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Feb 22, 2026
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