Stanford University
MS in Computer Science – Artificial Intelligence
Last reviewed June 2026 by the AI Graduate editorial team. Program data is compiled and verified from official university sources — see our methodology.
How this program compares
At an estimated $67.7K in total tuition, the MS in Computer Science – Artificial Intelligence sits about 60% above the $42.4K average for AI master's programs in our database — placing it in the 83rd percentile on cost among the 531 we track at this level. It is one of the 57% of programs in our database offered fully or partly online.
Admission Snapshot
Typical admitted student: A bachelor's degree in computer science or a related field with a strong GPA (typically 3.5+), programming experience, and relevant coursework in algorithms, data structures, and mathematics are required. GRE scores are optional, with strong letters of recommendation and research experience highly valued.
About This Program
Stanford's MS in Computer Science with an Artificial Intelligence specialization is among the most selective and influential AI degrees in the world. Sitting at the heart of Silicon Valley, the program gives students direct access to the faculty and labs — including the Stanford AI Lab (SAIL) — that helped define modern machine learning, computer vision, and natural language processing.
The AI track lets students go deep in areas such as deep learning, probabilistic modeling, robotics, and NLP while drawing on Stanford's broader strengths in systems and theory. Many students arrive with research ambitions or startup intentions, and the Bay Area network means internships and full-time roles at frontier labs and top companies are unusually accessible.
Why students choose it: Unmatched faculty, SAIL research access, and the strongest Silicon Valley hiring network of any AI program.
Career Outcomes
Position as AI Research Scientist in Cross-Industry AI with artificial intelligence expertise
- 1. Machine Learning Engineer
- 2. AI Research Scientist
- 3. Computer Vision Specialist
- 4. Natural Language Processing Engineer
What You'll Learn
- Design and implement machine learning models for real-world applications.
- Develop deep neural networks for tasks like image recognition and sequence modeling.
- Apply natural language processing techniques to analyze and generate human language.
- Build AI systems incorporating ethical considerations and automated reasoning.
Curriculum Highlights
The program features coursework in machine learning, natural language processing, computer vision, and the societal impacts of AI.
Top Employers
Top employers include Google, Meta, OpenAI, NVIDIA, and tech giants like Amazon and Microsoft.
Admissions
A bachelor's degree in computer science or a related field with a strong GPA (typically 3.5+), programming experience, and relevant coursework in algorithms, data structures, and mathematics are required. GRE scores are optional, with strong letters of recommendation and research experience highly valued.
Application Materials
- Statement of Purpose: Required
- Letters of Recommendation: 3
- Resume: Required
- Transcripts: Official transcripts required
Academic Requirements
- Degree Required: Bachelor's degree
- GRE/GMAT: Optional
- TOEFL/IELTS: Required for international students (TOEFL 100+ / IELTS 7.0+)
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