Comprehensive course analysis
About the course
Advances in artificial intelligence are impacting all aspects of daily life, and demand is on the rise for skilled engineers across a wide range of AI fields. The Artificial Intelligence Professional Program is designed for working professionals who want to dive into AI topics at graduate-level depth, but with additional flexibility of schedule and scope.
Modeled after the Stanford AI Graduate Certificate, the professional courses provide a rigorous introduction to machine learning, as well as opportunities to dive into theoretical and project-based learning in natural language processing and understanding.
All courses in the professional program are adapted from lectures and materials delivered in on-campus Stanford graduate courses. The professional courses feature enhanced support from Stanford-affiliated Course Assistants, all of whom have taken the courses and are working in industry.
Future offerings from the AI Graduate Certificate course list will be adapted to the AI Professional Program in consultation with Computer Science Dept. faculty.*
Earning the Certificate
You may earn a Stanford Professional Certificate in Artificial Intelligence by completing three courses within the program. Each course requires approximately 8-12 hours per week, depending on prior knowledge and experience with the material, and takes place over 10 weeks.
Continuing Education Units (CEUs)
You’ll earn 10 Continuing Education Units (CEUs) for each course completed within this certificate program. CEUs cannot be applied toward any Stanford degree. CEU transferability is subject to the receiving institution’s policies.
- College Calculus and Linear Algebra: You should be comfortable taking (multivariable) derivatives and understanding matrix/vector notation and operations.
- Basic Probability and Statistics: You should know the basics of probabilities, gaussian distributions, mean, and standard deviation.
- Natural Language Processing With Deep Learning
- Natural Language Understanding
- Machine Learning
Fields: machine learning, natural language processing. Topics: unsupervised learning, structured prediction, statistical learning theory, grounded language acquisition, compositional semantics, program induction. Learning semantics: Natural language allows us to express complex ideas using a fe...
Christopher Manning is a professor of computer science and linguistics at Stanford University and Director of the Stanford Artificial Intelligence Laboratory. He works on software that can intelligently process, understand, and generate human language material. He is a leader in applying Deep Lea...
Academic Appointments Professor, Linguistics Professor (By courtesy), Computer Science Member, Bio-X Administrative Appointments Professor, Department of Linguistics, Stanford University (2016 - Present) Director, Stanford Center for the Study of Language and Information (CSLI) (2013 - Present...
Bill MacCartney is a Senior Engineering Manager at Apple, where he leads the Siri Proactive Intelligence team in propagating predictive intelligence across the iOS platform and beyond. Bill is also a Consulting Professor of Computer Science at Stanford University, where he teaches CS224U, “Natura...
Andrew Ng is Co-founder of Coursera, and an Adjunct Professor of Computer Science at Stanford University. His machine learning course is the MOOC that had led to the founding of Coursera! In 2011, he led the development of Stanford University’s main MOOC (Massive Open Online Courses) platform an...
Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.