281 - 290 of 366 results for: CS

This course focuses on how robots can be effective teammates with other robots and human partners. Concepts and tools will be reviewed for characterizing task objectives, robot perception and control, teammate behavioral modeling, inter-agent communication, and team consensus. We will consider the application of these tools to robot collaborators, wearable robotics, and the latest applications in the relevant literature. This will be a project-based graduate course, with the implementation of algorithms in either python or C++.

This is an implementation-heavy, lab-based class that continues the topics from CS240LX. The labs will be more specialized, with an emphasis on research-worthy topics and techniques. The class format will follow CS240LX: two labs, twice a week, along with a set of research papers for context. Enrollment requires instructor permission.

Language shapes thought; for 40 years, software systems and some of their research challenges have been defined by the C language. In the past 5 years, this has begun to change, with new languages (Rust, Go, coq) becoming competitors to C in large classes of systems. CS340R is a project-centric course that examines how the Rust programming language changes software systems, solving some problems while presenting new ones. This course seeks to ask and start to answer a simple question: "What are the most important open research challenges for software systems written in Rust?"

This project-based course will provide a comprehensive overview of key requirements in the design and full-stack implementation of a digital health research application. Several pre-vetted and approved projects from the Stanford School of Medicine will be available for students to select from and build. Student teams learn about all necessary approval processes to deploy a digital health solution (data privacy clearance/I RB approval, etc.) and be guided in the development of front-end and back-end infrastructure using best practices. The final project will be the presentation and deployment of a fully approved digital health research application. CS106A, CS106B, Recommended: CS193P/A, CS142, CS47, CS110. Limited enrollment for this course. Apply for enrollment permission here: https://stanforduniversity.qualtrics.com/jfe/form/SV_8uGDTMoomnmJpn8

This class will cover the principles and practices of domain-specific programming models and compilers for dense and sparse applications in scientific computing, data science, and machine learning. We will study programming models from the recent literature, categorize them, and discuss their properties. We will also discuss promising directions for their compilation, including the separation of algorithm, schedule, and data representation, polyhedral compilation versus rewrite rules, and sparse iteration theory. Prerequisites: CS143 or equivalent

This is a design-studio course for the creation of domain-specific languages (DSLs). We will start with lectures teaching fundamental skills for designing and implementing DSLs, followed by a long term project designing and implementing a DSL of the student's choice. The course will particularly emphasize the role that languages can play in tasks that we do not usually think of as programming, such as DSLs for knitting patterns or geometric constructions.

This class could also be called "Build an Internet Router": Students work in teams of two to build a fully functioning Internet router, gaining hands-on experience building the hardware and software of a high-performance network system. Students design the control plane in C on a linux host and design the data plane in the new P4 language on both a software switch and a high-speed hardware switch (e.g., Intel Tofino). For the midterm milestone, teams must demonstrate that their routers can interoperate with the other teams by building a small scale datacenter topology. In the final 3-4 weeks of the class, teams will participate in an open-ended design challenge. Prerequisites: At least one student in each team must have taken CS144 at Stanford and completed Lab 3 (static router). No Verilog or FPGA programming experience is required. May be repeated for credit.

This course offers a hands-on experiences in building AI-powered robotic quadrupeds. Students will learn the state-of-the-art algorithms and techniques in robot learning and apply them in practice to build a fully functional quadruped. Specifically, the course consists of role-playing paper discussion and hands-on labs. A set of carefully curated papers will be selected at the beginning of the quarter. Each week, everyone reads the same paper, but a small number of student presenters take on specific roles for that paper. The role defines the lens through which the student reads the paper and what they bring to the discussion. Examples of roles include the journalist who summarizes the paper and its contributions based on facts, the paper reviewer who provides critical and insightful criticisms, and the archaeologist who determines where this paper sits in the context of previous and subsequent work. The lab portion of the course focuses on hands-on robot building, programming/training, and evaluation. Students will work in groups of four to develop their quadruped for their sole project in this course. A detailed weekly plan will be proposed and reviewed at the beginning of the quarter. Through weekly labs, students will work toward their final goals of creating an intelligent companion robot dog.Prerequisites: CS123 (or other approved robotics courses), CS107, MATH51

(Previously numbered CS376.) How will the future of human-computer interaction evolve? This course equips students with the major animating theories of human-computer interaction by connecting those theories to modern innovations. Major theories are drawn from interaction (e.g., tangible and ubiquitous computing), social computing (e.g., Johansen matrix), and design (e.g., reflective practitioner, wicked problems); they span domains such as AI+HCI (e.g., mixed initiative interaction), accessibility (e.g., ability based design), and interface software tools (e.g., threshold/ceiling diagrams). Students read and discuss multiple papers per week. Prerequisites: For CS and Symbolic Systems undergraduates/masters students, CS147 or CS247. No prerequisite for PhD students or students outside of CS and Symbolic Systems.