Artificial Intelligence Graduate Program
- Graduate Certificate
Fee may apply
"Artificial intelligence is the new electricity."
- Andrew Ng, Stanford Adjunct Professor
Take advantage of the opportunity to virtually step into the classrooms of Stanford professors like Andrew Ng who are leading the Artificial Intelligence revolution.
Classes in the Artificial Intelligence Graduate Program provide the foundation and advanced skills in the principles and technologies that underlie AI including logic, knowledge representation, probabilistic models, and machine learning. Students can pursue topics in depth, with courses available in areas such as robotics, vision, and natural language processing.
Prepare for advanced Artificial Intelligence curriculum and earn graduate credit by taking these recommended courses; these courses will not count towards the Artificial Intelligence Graduate Program. We highly recommend taking CS109 Introduction to Probability for Computer Scientists, or STATS116 Theory of Probability.
Note: Course offerings may be subject to change. You do not need to enroll in the program to take the courses. You may enroll in any courses if you meet its prerequisites.
Who Should Apply
Software engineers interested in artificial intelligence. The fast-paced, academically rigorous classes that are part of this program are appropriate for applicants who can demonstrate mastery of the prerequisite subject matter including statistics and probability, linear algebra and calculus. Students should also have significant programming experience in Java, C++, Python or similar languages.
As demand for AI courses is high and seats are limited, applications are subject to additional review. Applicants will be notified once the application review process is complete and a decision has been made.
Earning the Certificate
- Earn a Graduate Certificate in Artificial Intelligence
- Tailor the program to your interests and career goals
- Begin the program any academic quarter that an applicable course is offered, subject to prerequisites
- Take courses for graduate credit and a grade
- Receive a B (3.0) or better in each course
Need further planning guidance?
- Programming experience: C/C++ (CS108 or equivalent)
- Recommended: Linear algebra (Math104, Math113, CS205L or equivalent)
- Recommended: Statistics and probability (CS109 , STATS116 or equivalent)
- A conferred Bachelor’s degree with an undergraduate GPA of 3.0 or better.
To pursue a graduate program you need to apply.
Tuition is based on the number of units you take. See Graduate Course Tuition on our Tuition & Fees page for more information.
Time to Complete Certificate
1-2 years average
3 years maximum to complete
Submit an inquiry to receive more information.
Required (complete 1)
Elective (complete 3)
- Decision Making Under Uncertainty
- Principles of Robot Autonomy I
- Computational Logic
- Introduction to Robotics
- Natural Language Processing with Deep Learning
- Natural Language Understanding
- Probabilistic Graphical Models: Principles and Techniques
- Machine Learning
- Deep Learning
- Computer Vision: From 3D Reconstruction to Recognition
- Convolutional Neural Networks for Visual Recognition
- Reinforcement Learning
- Deep Generative Models
- Principles of Robot Autonomy II
- Deep Multi-task and Meta Learning
- Machine Learning Theory