# Courses at Stanford University relative to AI.

Systematized catalog of courses at Stanford relative to AI, Machine Learning, Optimization, Statistics, Control Theory, Computer Vision, and Natural Language Processing.

Stanford University provides various courses regarding constructing and tackling different mathematical models that are collectively called AI these days. In that post, I would like to share the catalog of classes relative to AI at Stanford. The note is based on a recommendation of different professors vision from Stanford, including Stephen Boyd, Percy Liang, Andrew Ng, Brad Osgood, and John Duchi.

Last update: **27-MAR-2022**.

Course Number |
Course Name |
Slides or Notes |
Videos |
---|---|---|---|

CS217 | Hardware Accelerators for Machine Learning | + | - |

CS231N | Convolution Neural Nets | + | + |

CS231M | Mobile Computer Vision | + | - |

CS231A | Computer Vision, From 3D Reconstruction to Recognition | + | - |

CS231B | CS231B: The Cutting Edge of Computer Vision | + | - |

CS230 | Deep Learning, A.Ng | - | + |

CS20SI | Tensorflow for DL Research | - | + |

STATS385 | Theories of Deep Learnig | + | + |

CS224N | Natural Language Processing with Deep Learning, C.Manning | + | + |

CS224U | Natural Language Understanding | - | + |

CS324 | Large Language Models | - | + |

CS224V | Conversational Virtual Assistants with Deep Learning | + | - |

CS331B | Representation Learning in Computer Vision, S.Savarese | - | + |

CS236 | Deep Generative Models | - | + |

CS330 | Deep Multi-Task and Meta Learning, C.Finn | + | + |

CS229 | Machine Learning, A.Ng | + | + |

CS221 | Artificial Intelligence: Principles and Techniques, P.Liang | - | + |

CS229T | Statistical Learning Theory, J.Duchi | - | + |

CS329S | Machine Learning Systems Design, C.Huyen | + | + |

STATS315A | Modern Applied Statistics: Elements of Statistical Learning, R.Tibshiran | - | - |

STATS315B | Modern Applied Statistics: Elements of Statistical Learning, J.Friedman | - | - |

CS224W | Machine Learning with Graphs, J.Leskovec | - | + |

CS228 | Probabilistic Graphical Models | - | + |

CS246 | Mining Massive Data Sets, J.Leskovec | + | - |

CS234 | Reinforcement Learning | - | + |

CS205A | Math methods for robotics, vision, graphics, D.James | - | + |

EE263 | Introduction to linear dynamic systems, S.Boyd | + | + |

EE364A | Convex Optimization I, S.Boyd | + | + |

EE364B | Convex Optimization II, S.Boyd | + | + |

CS329D | Machine Learning Under Distribution Shifts | - | - |

AA228 | Decision Making Under Uncertainty | - | - |

CS348I | Computer Graphics in the Era of AI | - | - |

CS223A | Introduction to Robotics, O.Khatib | + | + |

AA203 | Optimal and Learning-Based Control, M.Pavone | + | - |

AA274A | Principles of Robotic Autonomy I, M.Pavone | + | - |

AA274B | Principles of Robotic Autonomy II, M.Pavone | + | - |

CS428 | Computation and Cognition: The Probabilistic Approach | - | - |

MS&E314/CME336 | Conic Linear Optimization, Y.Ye | - | + |

MATH301 | Advanced topics in covnex optimization, E.Candes | - | - |

CS149 | Parallel Computing, K.Fatahalian, K.Olukotun | + | - |

Written on December 22, 2021