printer friendly pageCS 7: Personal Finance for Engineers
Introduction to the fundamentals and analysis specifically needed by engineers to make informed and intelligent financial decisions. Course will focus on actual industry-based financial information from technology companies and realistic financial issues. Topics include: behavioral finance, budgeting, debt, compensation, stock options, investing and real estate. No prior finance or economics experience required.
Terms: Aut
| Units: 1
Instructors:
Nash, A. (PI)
CS 11SI: How to Make VR: Introduction to Virtual Reality Design and Development
In this hands-on, experiential course, students will design and develop virtual reality applications. You'll learn how to use the Unity game engine, the most popular platform for creating immersive applications. The class will teach the design best practices and the creation pipeline for VR applications. Students will work in groups to present a final project in building an application for the Oculus Quest 2 headset. Enrollment is limited and by application only. See
https://cs11si.stanford.edu for more information and the link to the application. Prerequisite:
CS 106A or equivalent
Terms: Aut
| Units: 2
Instructors:
Borenstein, J. (PI)
CS 24: Minds and Machines (LINGUIST 35, PHIL 99, PSYCH 35, SYMSYS 1, SYMSYS 200)
(Formerly
SYMSYS 100). An overview of the interdisciplinary study of cognition, information, communication, and language, with an emphasis on foundational issues: What are minds? What is computation? What are rationality and intelligence? Can we predict human behavior? Can computers be truly intelligent? How do people and technology interact, and how might they do so in the future? Lectures focus on how the methods of philosophy, mathematics, empirical research, and computational modeling are used to study minds and machines. Students must take this course before being approved to declare Symbolic Systems as a major. All students interested in studying Symbolic Systems are urged to take this course early in their student careers. The course material and presentation will be at an introductory level, without prerequisites. If you have any questions about the course, please email symsys1staff@gmail.com.
Terms: Aut, Win, Sum
| Units: 4
| UG Reqs: GER:DB-SocSci, WAY-FR
Instructors:
Goodman, N. (PI)
;
Krejci, B. (PI)
;
Rose, D. (PI)
;
Wu, J. (PI)
;
Eigbe, N. (TA)
;
Fergesen, C. (TA)
;
Forster, M. (TA)
;
Graham, C. (TA)
;
Mon, K. (TA)
;
Ota, K. (TA)
;
Qing, C. (TA)
;
Ragupathi, A. (TA)
;
Schechtman, K. (TA)
;
Tong, N. (TA)
;
Xu, A. (TA)
;
Xu, R. (TA)
CS 25: Transformers United V4
Since their introduction in 2017, Transformers have taken the world by storm, and are finding applications all over Deep Learning. They have enabled the creation of powerful language models like ChatGPT and Gemini, and are a critical component in other ML applications such as text-to-image and video generation (e.g. DALL-E and Sora). They have significantly elevated the capabilities and impact of Artificial Intelligence. In
CS 25, which has become one of Stanford's hottest and most exciting seminars, we examine the details of how Transformers work, and dive deep into the different kinds of Transformers and how they're applied in various fields and applications. We do this through a combination of instructor lectures, guest lectures, and classroom discussions. Potential topics include LLM architectures, creative use cases (e.g. art and music), healthcare/biology and neuroscience applications, robotics and RL (e.g. physical tasks, simulations, or games), and so forth. We invite folks at
more »
Since their introduction in 2017, Transformers have taken the world by storm, and are finding applications all over Deep Learning. They have enabled the creation of powerful language models like ChatGPT and Gemini, and are a critical component in other ML applications such as text-to-image and video generation (e.g. DALL-E and Sora). They have significantly elevated the capabilities and impact of Artificial Intelligence. In
CS 25, which has become one of Stanford's hottest and most exciting seminars, we examine the details of how Transformers work, and dive deep into the different kinds of Transformers and how they're applied in various fields and applications. We do this through a combination of instructor lectures, guest lectures, and classroom discussions. Potential topics include LLM architectures, creative use cases (e.g. art and music), healthcare/biology and neuroscience applications, robotics and RL (e.g. physical tasks, simulations, or games), and so forth. We invite folks at the forefront of Transformers research for talks, which will also be livestreamed and recorded through YouTube/Zoom. Past speakers have included Andrej Karpathy, Geoffrey Hinton, Jim Fan, Ashish Vaswani, and folks from OpenAI, Google DeepMind, NVIDIA, etc. Our class includes social events and networking sessions and has a popular reception within and outside Stanford, with around 1 million total views on YouTube. This is a 1-unit S/NC course, where attendance is the only homework! Please enroll on Axess or audit by joining the livestream (or in person if seats are available). Prerequisites: basic knowledge of Deep Learning (should understand attention) or
CS224N/
CS231N/
CS230. Course website:
https://web.stanford.edu/class/cs25/
Terms: Aut, Spr
| Units: 1
CS 44N: Great Ideas in Graphics
A hands-on interactive and fun exploration of great ideas from computer graphics. Motivated by graphics concepts, mathematical foundations and computer algorithms, students will explore an eccentric selection of "great ideas" through short weekly programming projects. Project topics will be selected from a diverse array of computer graphics concepts and historical elements.
Terms: Aut
| Units: 3
Instructors:
James, D. (PI)
CS 80E: Dissecting The Modern Computer
In this course, students will be given a high-level, accessible introduction to computer architecture through the use of the RISC-V ISA. Through a series of interactive units, students will learn about the inner-workings of computers, from the execution of our programs all the way down to the hardware that runs them. Topics include simple digital circuits, assembly, simple processors, memory systems (Cache, DRAM, Disk), and bonus topics like GPU's. After completing this class, students should have a newfound appreciation for how incredible computational technology is, as well as direction to fantastic classes that delve into some of these topics in more detail, like
CS149,
EE108, and
EE180. Prerequisite:
CS106B.
Terms: Aut
| Units: 2
Instructors:
Master, T. (PI)
CS 100ACE: Problem-solving Lab for CS106A
Additional problem solving practice for the introductory CS course
CS 106A. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Limited enrollment, permission of instructor required. Concurrent enrollment in
CS 106A required.
Terms: Aut, Win, Spr
| Units: 1
Instructors:
King, E. (PI)
CS 100BACE: Problem-solving Lab for CS106B
Additional problem solving practice for the introductory CS course
CS106B. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Limited enrollment, permission of instructor required. Concurrent enrollment in
CS 106B required.
Terms: Aut, Win, Spr
| Units: 1
CS 103: Mathematical Foundations of Computing
What are the theoretical limits of computing power? What problems can be solved with computers? Which ones cannot? And how can we reason about the answers to these questions with mathematical certainty? This course explores the answers to these questions and serves as an introduction to discrete mathematics, computability theory, and complexity theory. At the completion of the course, students will feel comfortable writing mathematical proofs, reasoning about discrete structures, reading and writing statements in first-order logic, and working with mathematical models of computing devices. Throughout the course, students will gain exposure to some of the most exciting mathematical and philosophical ideas of the late nineteenth and twentieth centuries. Specific topics covered include formal mathematical proofwriting, propositional and first-order logic, set theory, binary relations, functions (injections, surjections, and bijections), cardinality, basic graph theory, the pigeonhole prin
more »
What are the theoretical limits of computing power? What problems can be solved with computers? Which ones cannot? And how can we reason about the answers to these questions with mathematical certainty? This course explores the answers to these questions and serves as an introduction to discrete mathematics, computability theory, and complexity theory. At the completion of the course, students will feel comfortable writing mathematical proofs, reasoning about discrete structures, reading and writing statements in first-order logic, and working with mathematical models of computing devices. Throughout the course, students will gain exposure to some of the most exciting mathematical and philosophical ideas of the late nineteenth and twentieth centuries. Specific topics covered include formal mathematical proofwriting, propositional and first-order logic, set theory, binary relations, functions (injections, surjections, and bijections), cardinality, basic graph theory, the pigeonhole principle, mathematical induction, finite automata, regular expressions, the Myhill-Nerode theorem, context-free grammars, Turing machines, decidable and recognizable languages, self-reference and undecidability, verifiers, and the P versus NP question. Students with significant proofwriting experience are encouraged to instead take
CS154. Students interested in extra practice and support with the course are encouraged to concurrently enroll in
CS103A. Prerequisite: CS106B or equivalent. CS106B may be taken concurrently with
CS103.
Terms: Aut, Win, Spr, Sum
| Units: 3-5
| UG Reqs: GER:DB-Math, WAY-FR
Instructors:
Aiken, A. (PI)
;
Lian, Z. (PI)
;
Liu, A. (PI)
;
Schwarz, K. (PI)
;
Agashe, R. (TA)
;
Aiu, K. (TA)
;
Anvari, M. (TA)
;
Cao, S. (TA)
;
Carrell, T. (TA)
;
Chang, T. (TA)
;
Chung, J. (TA)
;
Han, R. (TA)
;
Jee, Y. (TA)
;
Kung, B. (TA)
;
Pandya, D. (TA)
;
Raman, V. (TA)
;
Rodriguez, J. (TA)
;
Roman, E. (TA)
;
Saue- Fletcher, L. (TA)
;
Sotoudeh, M. (TA)
;
Xia, W. (TA)
;
Zhai, W. (TA)
CS 103ACE: Mathematical Problem-solving Strategies
Problem solving strategies and techniques in discrete mathematics and computer science. Additional problem solving practice for
CS103. In-class participation required. Prerequisite: consent of instructor. Co-requisite:
CS103.
Terms: Aut, Win, Spr
| Units: 1
Instructors:
Guan, R. (PI)
Filter Results: