Stanford - Artificial Intelligence Graduate Certificate

The courses below are required and/or recommended for completing the Artificial Intelligence Graduate Certificate Program 

Preparing

Mathematics

• Mathematics for Machine Learning series from Imperial College London [Coursera] 
• Stanford CS229 Linear Algebra Review and Reference [eBook] 
• Single Variable Calculus [MIT OpenCourseWare] 
• Multivariable Calculus [MIT OpenCourseWare] 
• MIT Linear Algebra course [MIT OpenCourseWare] 
• Mathematics of Machine Learning [MIT OpenCourseWare] 
• The Matrix calculus for Deep Learning [PDF] 

Optimization

• Machine Learning Refined: Foundations, Algorithms, and Applications (Book) 
• Linear Algebra and Optimization for Machine Learning: A Textbook (Book) 
• Convex Optimization (Book) 
• Numerical Optimization (Book) 

Statistics and Probability

• Stanford CS229 Review of Probability Theory [PDF] 
• Stanford CS229 Statistics and Probability Refresher 

Python

• Introduction to Computer Science and Programming Using Python [edX] 
• https://www.coursera.org/specializations/data-science-python 
• Stanford CS231n Python/Numpy Tutorial 

Courses (Fee may apply)

• CS221: Artificial Intelligence: Principles and Techniques 
• AA228: Decision Making Under Uncertainty 
• AA274A: Principles of Robot Autonomy I 
• CS157: Computational Logic 
• CS223A: Introduction to Robotics 
• CS224N: Natural Language Processing w/ Deep Learning 
• CS224U: Natural Language Understanding 
• CS228: Probabilistic Graphical Models: Principles and Techniques 
• CS229: Machine Learning 
• CS230: Deep Learning 
• CS231A: Computer Vision: From 3D Reconstruction to Recognition 
• CS231N: Convolutional Neural Networks for Visual Recognition 
• CS234: Reinforcement Learning 
• CS236: Deep Generative Models 
• CS237B: Principles of Robot Autonomy II 
• CS330: Deep Multi-task and Meta Learning 
• STATS214: Machine Learning Theory