10-747 Neuro-Symbolic AI

12 units, Carnegie Mellon University

The course explores the emerging field and arguably the next frontier/paradigm in AI: Neuro-Symbolic AI, which combines the modeling strengths of statistical/neural/deep learning with the reasoning and structured representational strengths of symbolic systems. Future breakthroughs in reliable reasoning will most likely rely on such an approach. An example of Neuro-Symbolic AI already in use is AlphaGeometry, an AI capable of solving Olympiad-level geometry problems. We will cover theoretical foundations, key methodologies, and practical applications of neuro-symbolic AI. The course will be structured as a partial seminar course. Of the two classes each week, one class will be a traditional lecture, while the other class will be a student-led reading-seminar on key papers, centering around the topic of that week's lecture.

Prerequisites: 10-301 (min C) or 10-315 (min C) or 10-715 (min C) or 10-701 (min C) or 10-601 (min C)

Offered: Fall 2025

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