251 - 260 of 366 results for: CS

Artificial Intelligence (AI) measurement science provides frameworks and methodologies for evaluating, benchmarking, and understanding AI systems. As AI systems become increasingly powerful and deploy into high-stakes domains, the need for rigorous measurement approaches has grown in importance. Current measurement approaches are often ad hoc, lacking theoretical grounding, and failing to connect to real-world use cases. This has led to a measurement crisis characterized by benchmark saturation, inconsistent evaluation methodologies, and difficulty in making valid claims about AI capabilities. This course will cover the foundations of AI measurement science from first principles and outline connections to the growing literature on the topic. This includes: validity theory as applied to AI evaluation, focusing on content, criterion, construct, external, and consequential validity; psychometric models for AI measurement, including item response theory and latent variable models; scaling laws and intervention effects, predicting the impacts of data, computing, and architecture choices; synthetic data generation for evaluation and its implications; governance and policy considerations around AI measurement. This is a graduate-level course. By the end of the course, students should be able to understand, implement, and critique state-of-the-art AI measurement approaches and be ready to conduct research on these topics.

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

This course examines how advances in AI are transforming the economy and reshaping the frontier of entrepreneurship. In Spring 2026, the course will emphasize venture creation: students will explore how large language models and other AI tools enable small teams to build products and companies with unprecedented speed and scale. Each week features guest speakers who are leaders in AI, business, economics, and industry, alongside discussion of cutting-edge research and its practical implications. Working in interdisciplinary teams, students will develop, prototype, and refine an AI-enabled product or startup concept, culminating in a final presentation of a working prototype and venture strategy. Designed primarily for graduate students in economics, business, computer science, and related fields. Admission is by application only. To learn more and apply, please visit https://digitaleconomy.stanford.edu/about/education/the-ai-awakening-implications-for-the-economy-and-society/; applications close at 5pm PT on March 16, 2026, with priority given to those received by March 9.

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.

Intellectual history of computational linguistics, natural language processing, and speech recognition, using primary sources. Reading of seminal early papers, interviews with early pioneers, with the goal of understanding the origins and intellectual development of the field. Prerequisites: (strictly required) completion of a Stanford graduate NLP course ( CS 224C/N/U/S, 329X, 384).

The sustainable development goals (SDGs) encompass many important aspects of human and ecosystem well-being that are traditionally difficult to measure. This project-based course will focus on ways to use inexpensive, unconventional data streams to measure outcomes relevant to SDGs, including poverty, hunger, health, governance, and economic activity. Students will apply machine learning techniques to various projects outlined at the beginning of the quarter. The main learning goals are to gain experience conducting and communicating original research. Prior knowledge of machine learning techniques, such as from CS 221, CS 229, CS 231N, STATS 202, or STATS 216 is required. Open to both undergraduate and graduate students. Enrollment limited to 24. Students must apply for the class by filling out the form at https://goo.gl/forms/9LSZF7lPkHadix5D3. A permission code will be given to admitted students to register for the class.

This course provides a survey of the most important and influential concepts in autonomous robotic manipulation. It includes classical concepts that are still widely used and recent approaches that have changed the way we look autonomous manipulation. We cover approaches towards motion planning and control using visual and tactile perception as well as machine learning. This course is especially concerned with new approaches for overcoming challenges in generalization from experience, exploration of the environment, and learning representation so that these methods can scale to real problems. Students are expected to present one paper in a tutorial, debate a paper once from the Pro and once from the Con side. They are also expected to propose an original research project and work on it towards a research paper. Recommended: CS 131, 223A, 229 or equivalents.

Advanced control methodologies and novel design techniques for complex human-like robotic and bio mechanical systems. Class covers the fundamentals in operational space dynamics and control, elastic planning, human motion synthesis. Topics include redundancy, inertial properties, haptics, simulation, robot cooperation, mobile manipulation, human-friendly robot design, humanoids and whole-body control. Additional topcs in emerging areas are presented by groups of students at the end-of-quarter mini-symposium. Prerequisites: 223A or equivalent.

Theoretical foundations of modern techniques at the intersection of causal inference and machine learning. Topics may include: semi-parametric inference and semi-parametric efficiency, modern statistical learning theory, Neyman orthogonality and double/debiased machine learning, theoretical foundations of high-dimensional linear regression, theoretical foundations of non-linear regression models, such as random forests and neural networks, adaptive non-parametric estimation of conditional moment models, estimation and inference on heterogeneous treatment effects, causal inference and reinforcement learning, off-policy evaluation, adaptive experimentation and inference.