A tutorial on uncertainty estimation in ML for a non-research practitioners of machine learning. Will cover basics of uncertainty estimation via ensemble methods, ensemble-emulation methods and generative methods. We will also provide a detailed case-study of an example usage of uncertainty estimation.

Uncertainty Estimation in ML

Natural Language Processing (NLP) is a domain of research whose objective is to analyze and understand human languages and develop technologies to enable human machine interactions with natural languages. NLP is an interdisciplinary field involving linguistics, computer sciences and artificial intelligence. The goal of this course is to provide students with comprehensive knowledge of NLP. Students will be equiped with the principles and theories of NLP, as well as various NLP technologies, including rule-based, statistical and neural network ones. After this course, students will be able to conduct NLP research and develop state-of-the-art NLP systems.

Natural Language Processing course (stream 1, autumn 2021)

Особенности применения NLP в практических задачах

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Uncertainty Estimation in ML

Natural Language Processing course (stream 1, autumn 2021)

NLP in practice