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