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- Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning
- Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering
- Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection
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