CS 309: Industrial Lectureships in Computer Science
Guest computer scientist. By arrangement. May be repeated for credit.
| Repeatable
for credit
CS 309A: Cloud Computing Seminar
For science, engineering, computer science, business, education, medicine, and law students. Cloud computing is bringing information systems out of the back office and making it core to the entire economy. Furthermore with the advent of smarter machines cloud computing will be integral to building a more precision planet. This class is intended for all students who want to begin to understand the implications of this technology. Guest industry experts are public company CEOs who are either delivering cloud services or using cloud services to transform their businesses.
Terms: Aut
| Units: 1
| Repeatable
for credit
Instructors:
Chou, T. (PI)
CS 315B: Parallel Computing Research Project
Advanced topics and new paradigms in parallel computing including parallel algorithms, programming languages, runtime environments, library debugging/tuning tools, and scalable architectures. Research project. Prerequisite: consent of instructor.
Last offered: Autumn 2022
CS 316: Advanced Multi-Core Systems
In-depth coverage of the architectural techniques used in modern, multi-core chips for mobile and server systems. Advanced processor design techniques (superscalar cores, VLIW cores, multi-threaded cores, energy-efficient cores), cache coherence, memory consistency, vector processors, graphics processors, heterogeneous processors, and hardware support for security and parallel programming. Students will become familiar with complex trade-offs between performance-power-complexity and hardware-software interactions. A central part of CS316 is a project on an open research question on multi-core technologies. Prerequisites:
EE 180 (formerly 108B) and
EE 282. Recommended:
CS 149.
Last offered: Spring 2019
CS 319: Topics in Digital Systems
Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
| Repeatable
for credit
CS 320: Value of Data and AI
Many of the most valuable companies in the world and the most innovative startups have business models based on data and AI, but our understanding about the economic value of data, networks and algorithmic assets remains at an early stage. For example, what is the value of a new dataset or an improved algorithm? How should investors value a data-centric business such as Netflix, Uber, Google, or Facebook? And what business models can best leverage data and algorithmic assets in settings as diverse as e-commerce, manufacturing, biotech and humanitarian organizations? In this graduate seminar, we will investigate these questions by studying recent research on these topics and by hosting in-depth discussions with experts from industry and academia. Key topics will include value of data quantity and quality in statistics and AI, business models around data, networks, scaling effects, economic theory around data, and emerging data protection regulations. Students will also conduct a group research projects in this field.nnPrerequisites: Sufficient mathematical maturity to follow the technical content; some familiarity with data mining and machine learning and at least an undergraduate course in statistics are recommended.
Last offered: Winter 2022
CS 322: Triangulating Intelligence: Melding Neuroscience, Psychology, and AI (PSYCH 225)
This course will cover both classic findings and the latest research progress on the intersection of cognitive science, neuroscience, and artificial intelligence: How does the study of minds and machines inform and guide each other? What are the assumptions, representations, or learning mechanisms that are shared (across multiple disciplines, and what are different? How can we build a synergistic partnership between cognitive psychology, neuroscience, and artificial intelligence? We will focus on object perception and social cognition (human capacities, especially in infancy and early childhood) and the ways in which these capacities are formalized and reverse-engineered (computer vision, reinforcement learning). Through paper reading and review, discussion, and the final project, students will learn the common foundations shared behind neuroscience, cognitive science, and AI research and leverage them to develop their own research project in these areas. Recommended prerequisites:
PSYCH 1, PSYCH 24/
SYMSYS 1/
CS 24,
CS 221,
CS 231N
Last offered: Winter 2022
CS 323: The AI Awakening: Implications for the Economy and Society
Intelligent computer agents must reason about complex, uncertain, and dynamic environments. This course is a graduate level introduction to automated reasoning techniques and their applications, covering logical and probabilistic approaches. Topics include: logical and probabilistic foundations, backtracking strategies and algorithms behind modern SAT solvers, stochastic local search and Markov Chain Monte Carlo algorithms, variational techniques, classes of reasoning tasks and reductions, and applications. Enrollment by application:
https://digitaleconomy.stanford.edu/about/the-ai-awakening-implications-for-the-economy-and-society/
Terms: Spr
| Units: 3-4
Instructors:
Brynjolfsson, E. (PI)
CS 323A: The AI Awakening: Implications for the Economy and Society
This course offers an overview of blockchain governance and DAOs, including the governance of layer-1 blockchains, DAO tooling, on-chain and off-chain voting, delegation and constitutional design, identity, and privacy. We will cover these topics both from a technical perspective and from a social scientific perspective, and will include a range of guests from the web3 space.
CS 324: Advances in Foundation Models
Foundation models (FMs) are transforming the landscape of AI in research and industry. Such models (e.g., GPT-3, CLIP, Stable Diffusion) are trained on large amounts of broad data and are adaptable to a wide range of downstream tasks. In this course, students will learn fundamentals behind the models and algorithms, systems and infrastructure, and ethics and societal impacts of foundation models, with an emphasis on gaining hands-on experience and identifying real-world use-cases for FMs. Students will hear from speakers in industry working on foundation models in the wild. The main class assignment will be a quarter-long final project, involving either researching the capabilities of FMs or building an FM-powered application.
Last offered: Winter 2023
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