Not available at this time

Not available at this time

Not available at this time

Not available at this time

Not available at this time

Not available at this time

Weekly seminar requiring students to read an NLP paper and discuss and review it from a variety of perspectives. Some weeks will feature invited speakers instead of paper reviews.

This course introduces graduate and undergraduate students to concepts, practices, and tools for conducting effective research. Students will learn basic methods for activities such as reading technical papers, selecting research topics, devising research questions, planning research, analyzing experimental results, modeling and simulation of computational phenomena, and synthesizing broader theories.

The real-world deployment of machine learning models faces a series of lateral challenges affecting model trustworthiness, such as domain generalization, dataset shifts, causal validity, explainability, fairness, representativeness, and transparency. These challenges become increasingly important in techno-social systems affecting human high-stake decision making, which is often regulated by law. In this course, students will learn techniques for robust model evaluation, model selection, causal discovery, explainable and fair artificial intelligence, and interpretable models.

Not available at this time