Differentiable optimization and generative AI for combinatorial problems. Theoretical Foundations:
Students flock to CS331/EE363 not just for the subject matter, but for Boyd’s unique pedagogical style. He is known for a teaching philosophy that prioritizes the "hands-on" approach. While many graduate theory courses descend into abstract proofs that have little relevance to practical implementation, Boyd’s version of Linear Dynamical Systems is relentlessly practical. He encourages the use of high-level modeling languages like CVX (a modeling system he co-developed) to solve complex problems immediately, bridging the gap between theoretical mathematics and engineering application.
Two fundamental concepts in control theory are controllability and observability. Is it possible to steer a system from any initial state to any desired final state? Can the internal state of a system be determined by observing its outputs? These concepts are rigorously defined using Linear Matrix Inequalities (LMIs), providing students with a powerful toolkit for analyzing complex networks.
Reading and discussing recent research papers rather than following a static textbook. cs331 stanford
At Stanford University , the course number typically refers to advanced, graduate-level research seminars that evolve to cover the latest breakthroughs in Artificial Intelligence.
Using machine learning to discover novel procedures that outperform classical ones in specific domains.
Last updated: 2026. Course offerings and professors are subject to change. Always refer to Stanford’s official Explore Courses portal for the most current scheduling. While many graduate theory courses descend into abstract
At its core, deals with systems that evolve over time. While the prerequisites suggest a strong background in linear algebra (specifically the legendary EE263: Introduction to Linear Dynamical Systems), this course pushes far beyond the basics.
If you are not yet a Stanford student but are targeting in a future quarter, here is a self-study roadmap:
Because CS 331 is a flexible advanced seminar number, it has historically served as a home for various high-level research topics led by renowned faculty: Is it possible to steer a system from
Configure existing parameters of algorithms to better suit specific application domains.
The core philosophy of the modern CS 331 is that we no longer need to rely solely on human intuition to build efficient systems. In practice, we often have vast amounts of data about the specific environments where an algorithm will run. CS 331 investigates how to leverage that data to: