Stanford - Artificial Intelligence Graduate Certificate
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