Stanford - Artificial Intelligence Graduate Certificate
The courses below are required and/or recommended for completing the Artificial Intelligence Graduate Certificate Program (opens in a new tab)
Preparing
Mathematics
- Mathematics for Machine Learning series from Imperial College London [Coursera] (opens in a new tab)
- Stanford CS229 Linear Algebra Review and Reference [eBook] (opens in a new tab)
- Single Variable Calculus [MIT OpenCourseWare] (opens in a new tab)
- Multivariable Calculus [MIT OpenCourseWare] (opens in a new tab)
- MIT Linear Algebra course [MIT OpenCourseWare] (opens in a new tab)
- Mathematics of Machine Learning [MIT OpenCourseWare] (opens in a new tab)
- The Matrix calculus for Deep Learning [PDF] (opens in a new tab)
Optimization
- Machine Learning Refined: Foundations, Algorithms, and Applications (Book) (opens in a new tab)
- Linear Algebra and Optimization for Machine Learning: A Textbook (Book) (opens in a new tab)
- Convex Optimization (Book) (opens in a new tab)
- Numerical Optimization (Book) (opens in a new tab)
Statistics and Probability
- Stanford CS229 Review of Probability Theory [PDF] (opens in a new tab)
- Stanford CS229 Statistics and Probability Refresher (opens in a new tab)
Python
- Introduction to Computer Science and Programming Using Python [edX] (opens in a new tab)
- https://www.coursera.org/specializations/data-science-python (opens in a new tab)
- Stanford CS231n Python/Numpy Tutorial (opens in a new tab)
Courses (Fee may apply)
- CS221: Artificial Intelligence: Principles and Techniques (opens in a new tab)
- AA228: Decision Making Under Uncertainty (opens in a new tab)
- AA274A: Principles of Robot Autonomy I (opens in a new tab)
- CS157: Computational Logic (opens in a new tab)
- CS223A: Introduction to Robotics (opens in a new tab)
- CS224N: Natural Language Processing w/ Deep Learning (opens in a new tab)
- CS224U: Natural Language Understanding (opens in a new tab)
- CS228: Probabilistic Graphical Models: Principles and Techniques (opens in a new tab)
- CS229: Machine Learning (opens in a new tab)
- CS230: Deep Learning (opens in a new tab)
- CS231A: Computer Vision: From 3D Reconstruction to Recognition (opens in a new tab)
- CS231N: Convolutional Neural Networks for Visual Recognition (opens in a new tab)
- CS234: Reinforcement Learning (opens in a new tab)
- CS236: Deep Generative Models (opens in a new tab)
- CS237B: Principles of Robot Autonomy II (opens in a new tab)
- CS330: Deep Multi-task and Meta Learning (opens in a new tab)
- STATS214: Machine Learning Theory (opens in a new tab)