71 - 80 of 366 results for: CS

Recent advances in natural language processing (NLP), especially around large pretrained models, have enabled extensive successful applications. However, there are growing concerns about the negative aspects of NLP systems, such as biases and a lack of input from users. This course gives an overview of human-centered techniques and applications for NLP, ranging from human-centered design thinking to human-in-the-loop algorithms, fairness, and accessibility. Along the way, we will cover machine-learning techniques which are especially relevant to NLP and to human experiences. Prerequisite: CS224N or CS224U, or equivalent background in natural language processing. Prerequisite: CS224N or CS224U, or equivalent background in natural language processing.

Computer Vision technologies are transforming automotive, healthcare, manufacturing, agriculture and many other sections. Today, household robots can navigate spaces and perform duties, search engines can index billions of images and videos, algorithms can diagnose medical images for diseases, and smart cars can see and drive safely. Lying in the heart of these modern AI applications are computer vision technologies that can perceive, understand, and reconstruct the complex visual world. This course is designed for students who are interested in learning about the fundamental principles and important applications of Computer Vision. This course will introduce a number of fundamental concepts in image processing and expose students to a number of real-world applications. It will guide students through a series of projects to implement cutting-edge algorithms. There will be optional discussion sections on Fridays. Prerequisites: Students should be familiar with Python, Calculus & Linear Algebra.

How is AI accelerating breakthroughs in fields like biotechnology, neuroscience, and cybersecurity? How are other fields (semiconductors, material sciences advances) influencing the development of AI? What opportunities and challenges arise as these domains converge? These are some of the key questions this course addresses. This course brings the Stanford Emerging Technology Review, a pioneering partnership between the School of Engineering, Institute for Human-Centered AI, and the Hoover Institution, into the classroom - offering a technically grounded, human-centered and interdisciplinary examination of key emerging technology areas like cybersecurity, biotechnology, neuroscience, space technologies, and material sciences, and their intersection with AI advancements that shape the future of science, engineering, and society. Each week, a leading Stanford faculty or fellow will present a seminar on one or multiple domains, emphasizing foundational concepts and computational challenges, current technical advances with AI, and over-the-horizon implications for individual users, communities, and broader society. The course is structured to balance technical breadth and depth, offering students an integrated view of technical innovation and its real-world consequences. Emphasis is placed on understanding both the mechanics of each technology and the broader factors that influence its development and adoption, such as economic incentives, security risks, and ethical considerations. This course is ideal for upper-level undergraduate and graduate students who want to engage with cutting-edge research, think critically about the future of technology and their research, and interact directly with experts at the intersection of science, society, and policy.

Basic principles for endowing mobile autonomous robots with perception, planning, and decision-making capabilities. Algorithmic approaches for robot perception, localization, and simultaneous localization and mapping; control of non-linear systems, learning-based control, and robot motion planning; introduction to methodologies for reasoning under uncertainty, e.g., (partially observable) Markov decision processes. Extensive use of the Robot Operating System (ROS) for demonstrations and hands-on activities. Prerequisites: CS 106A or equivalent, CME 100 or equivalent (for linear algebra), and CME 106 or equivalent (for probability theory).

Artificial Intelligence technology can and must be guided by human concerns. The course examines how mental models and user models of AI systems are formed, and how that leads to user expectations. This informs a set of design guidelines for building AI systems that are trustworthy, understandable, fair, and beneficial. The course covers the impact of AI systems on the economy and everyday life, and ethical issues of collecting data and running systems, including respect for persons, beneficence, fairness and justice.

Covers key concepts in computer systems through the lens of operatingnsystem design and implementation. Topics include threads, scheduling,nprocesses, virtual memory, synchronization, multi-core architectures,nmemory consistency, hardware atomics, memory allocators, linking, I/O,nfile systems, and virtual machines. Concepts are reinforced with fournkernel programming projects in the Pintos operating system. This classnmay be taken as an accelerated single-class alternative to the CS111,nCS112 sequence; conversely, the class should not be taken by studentsnwho have already taken CS111 or CS112

Students will implement a simple, clean operating system (virtual memory, processes, file system) in the C programming language, on a rasberry pi computer and use the result to run a variety of devices and implement a final project. All hardware is supplied by the instructor, and no previous experience with operating systems, raspberry pi, or embedded programming is required.Enrollment is by application: https://forms.gle/1UuHUJsWSRGLSH8BA

This course introduces undergraduates to data analytics and AI, using sports as the motivating application. Through real-world examples from professional sports, students will explore concepts such as exploratory data analysis, regression, classification, clustering, dimensionality reduction, and neural networks. Weekly assignments and a final team project will develop students' skills in using Python-based tools for sports analytics. A light final exam tests key conceptual understanding. Prerequisite: CS106A (or equivalent Python background) and CS109 (or equivalent probability background). TA sessions will cover use of pandas and other libraries students will use for projects, as well as freely available sports datasets that could be used.

Concepts and techniques used in constructing interactive web applications. Browser-side web facilities such as HTML, cascading stylesheets, the document object model, and JavaScript frameworks and Server-side technologies such as server-side JavaScript, sessions, and object-oriented databases. Issues in web security and application scalability. New models of web application deployment. Prerequisite: CS 107.