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 - +
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