Interpreting Machine Learning Models
This course provides an introduction to interpreting machine learning models, specifically deep learning models that are pre-trained on large amounts of data using self-supervision. The course is designed to cover the breadth of different techniques for explaining predictions of classifiers trained on language, vision, and tabular data. You will get hands-on experience working with different algorithms for interpretability and benchmark widely used ML models for faithfulness and plausibility. The course will also cover the basics of how some explanations can help uncover artifacts and use those observations to build robust ML models.
Course taught by expert instructors
Nazneen Rajani
Robustness Research Lead at Hugging Face
Nazneen is a Robustness Research Lead at Hugging Face She got her Ph.D. in Computer Science from UT Austin where she was advised by Pof. Ray Mooney. Several of her works (15+) have been published in top-tier AI conferences including ACL, EMNLP, NAACL, NeurIPS, ICLR. Nazneen was one of the finalists for the VentureBeat Transform 2020 women in AI Research. Her work has been covered by various media outlets including Quanta magazine, VentureBeat, SiliconAngle, ZDNet, Datanami. More details about her work can be found on her website https://www.nazneenrajani.com/
The course
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Flexible schedule for busy professionals
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