BS in Symbolic Systems - Artificial Intelligence Concentration

Stanford University

This interdisciplinary program explores the relationship between natural and artificial systems, combining computer science, psychology, philosophy, and linguistics.

What You'll Learn

• Design and implement neuro-symbolic systems that combine deep learning with logical reasoning for flexible problem-solving and transfer learning across domains.[1] • Apply compositional learning frameworks to build agents capable of understanding visual concepts, natural language instructions, and robotic control tasks.[1] • Develop algorithms for continual learning and concept acquisition from multimodal data streams with data-efficient training methods.[1] • Integrate symbolic program execution with neural perception to enable question answering, reasoning about unseen tasks, and human-AI instruction interpretation.[1]

Typical Courses

1. Machine Learning and Neural Networks 2. Symbolic Reasoning and Logic Programming 3. Computer Vision and Visual Perception 4. Natural Language Processing 5. Robotics and Autonomous Agents 6. Knowledge Representation and Reasoning

Career Paths

1. Machine Learning Engineer 2. AI Research Scientist 3. Robotics Engineer 4. AI/ML Product Manager