COSC 89.38 Topics in Neurosymbolic AI

This seminar explores cutting-edge research in Natural Language Processing (NLP), with a focus on large language models (LLMs), interpretability, alignment, multimodality, and the integration of neural and symbolic reasoning. The course emphasizes next-generation models that combine the strengths of deep learning and structured reasoning (Neurosymbolic Learning). Students will examine how hybrid systems can enable interpretable, data-eCicient, and reliable reasoning in language and multimodal domains.

The class operates as a student-led research seminar. Weekly meetings center on reading and discussing recent papers from ACL, EMNLP, NeurIPS, ICLR, and related venues. Short lectures provide conceptual and methodological context (e.g., diCerentiable logic, program induction, constraint-based learning). Evaluation is based on discussion leadership and participation, weekly reflections, and a semester project with defined milestones that extends, critiques, or applies one of the covered research areas.

Degree Requirement Attributes

Dist:TAS

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Department-Specific Course Categories

Computer Science