Courses at Stanford relative to AI


This is a post about systematizing catalog of course at Stanford relative to AI/ML/Optimization/STATs/Control.


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, John Duchi.

Last update: 22-DEC-2021.

Course Number Course Name Slides or Notes Videos
CS231N Convolution Neural Nets + +
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 - +
CS224V Conversational Virtual Assistants with Deep Learning + -
CS324 Understanding and Developing Large Language Models - -
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 - +
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 I, D.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 ptimal 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 - -

Written on December 22, 2021