Courses at Stanford University not relative to AI.

Systematized catalog of courses at Stanford relative to creating various systems by leveraging science and engineering from various fields.

Stanford University provides various courses regarding constructing and tackling different mathematical models for constructing engineering systems. Some of them with personal bias are listed below. They do not have an AI prefix or suffix in it.

Nevertheless, it’s upgradable to basic Machine Learning with one (not easy) step: You localize the heuristic and replace it with an ML data-driven approach.

You can for sure do this if the methodology is not rigorous. In fact, sometimes, people do it even if the methodology is rigorous, but computation demands for correct solving mathematical problems is enormously hard.

Course Number Course Name Slides or Notes Videos
EE261 EE261 The Fourier Transform and its Applications + +
CS148 CS148 Introduction to Computer Graphics and Imaging + -
CS248 CS248 Interactive Computer Graphics + -
CS348A CS348A Computer Graphics Geometric Modeling Processing + -
CS268 CS268 Geometric Algorithms + -
CS348B CS348B Image Synthesis + -
EE368 EE368 Digital Image Processing + -
EE264 EE264 Digital Signal Processing + -
CS143 CS143 Compilers class + -
CS243 CS243 Advanced Compilers class (Program Analysis and Optimization) + -
AA2000 AA200 Applied Aerodynamics + -
AA210 AA210A Fundamentals of Compressible Flow + -
AA242A AA242A Classical Dynamics + -
CME308 CME308 Stochastic Methods in Engineering + -
CS205A CS205A Mathematical Methods for Robotics, Vision, and Graphics + -
CS223A CS223A Introduction to Robotics + +
CS255A CS225A Experimental Robotics + -
CS327A CS327A Advanced Robotic Manipulation + -
ENGR105 ENGR105 Feedback Control Design + -
MS&E211 MS&E310 Linear and Nonlinear Optimization + -
CME204 CME204 Partial Differential Equations in Engineering + -
CME302 CME302 Numerical Linear Algebra + -
CME304 CME304 Numerical Optimization + -
CS245 CS245 Principles of Data-Intensive Systems + -
Link Introduction to Databases + -
CS348V CS348V Visual Computing Systems (with some aspects of Deep Learning) + -
CS149 CS149 Parallel Computing + -
CS265 CS265 Randomized Algorithms and Probabilistic Analysis + -

p.s. There is a vision of Prof. John C. Duchi that he has shared in Simons Institute during Statistical Theory, Privacy, and Data Analysis, APR-2019 at 09:51 that in fact there are a lot of disciplines that underpin AI.

And there are people in academia (John C. Duchi is an example of that) that cannot pronounce: “AI is everything, AI solves everything.”

In any case, I hope the catalog above will be useful for people who do science and engineering. For example, EE261 is an essential core for understanding and creating devices and approaches in:

  • Digital Signal Processing.
  • Digital Filters.
  • Compressed Sensing.
  • Music Composition. Catalog of courses relative to Music at Stanford
  • Medical Images.

Written on December 22, 2021