Stanford University

How this program compares

Budget-wise, $270.9K puts this bachelor's program at the 96th percentile on cost — about 202% above the $89.8K average across 186 comparable listings. It is one of the 54% of programs in our database offered fully or partly online.

Admission Snapshot

Typical admitted student: Applicants must have a high school diploma with strong academic performance (GPA typically 3.8+) and competitive standardized test scores (SAT 1470+ or ACT 33+). Stanford's admission process is highly selective and considers extracurricular achievements, essays, and demonstrated interest in computer science and artificial intelligence.

About This Program

This interdisciplinary program explores the relationship between natural and artificial systems, combining computer science, psychology, philosophy, and linguistics. Coursework concentrates on Machine Learning and Natural Language Processing (NLP). Most students complete it in about 4 years.

Graduates frequently move into roles such as AI Research Scientist, with reported salaries around $135,000.

Design and implement neuro-symbolic systems that combine deep learning with logical reasoning for flexible problem-solving and transfer learning across domains.

Budget planners should note $270.9K estimated total tuition (96th percentile among AI bachelor's programs, above the $89.8K average). Applicants should weigh that premium against the program's outcomes and brand.

It is one of 65 AI-related programs we track in California, of which about 37% offer an online option. On price, it comes in higher than about 97% of the California programs in our data, where typical tuition runs near $63.3K.

Career Outcomes

The BS in Symbolic Systems - Artificial Intelligence Concentration at Stanford University (Stanford, California) is oriented toward AI Research Scientist hiring, with alumni reporting pay near $135,000. Its focus on Machine Learning and Natural Language Processing (NLP) maps directly to how employers screen for specialized skills rather than generic degrees. Federal projections for this occupational area point to roughly 21% growth this decade — verify the current figure on the BLS Occupational Outlook Handbook before you rely on it.

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

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]

Curriculum Highlights

The concentration includes core courses in logic and probability, plus advanced study in machine learning, natural language processing, and neural networks.

Top Employers

Top employers include Google, OpenAI, DeepMind, Microsoft, Meta, and leading robotics companies like Boston Dynamics and Tesla.

Admissions

Admission to Stanford University's BS in Symbolic Systems - Artificial Intelligence Concentration generally expects a bachelor of science (bs). The GRE is not required here — a growing norm among AI programs — so applicants can often lead with coursework, projects and recommendations instead. Deadlines, testing policies and funding change year to year, so confirm the current requirements on the official program page before applying.

Application Materials

• Personal Essays: Required
• Letters of Recommendation: 2–3
• Transcripts: Official high school transcripts required
• Standardized Test Scores: SAT or ACT required

Frequently Asked Questions