1 - 10 of 249 results for: CS

For those with limited experience with computers or who want to learn more about Stanford's computing environment. Topics include: computer maintenance and security, computing resources, Internet privacy, and copyright law. One-hour lecture/demonstration in dormitory clusters prepared and administered weekly by the Resident Computer Consultant (RCC). Final project. Not a programming course.

A practical introduction to using the Unix operating system with a focus on Linux command line skills. Class will consist of video tutorials and weekly hands-on lab sections. Topics include: grep and regular expressions, ZSH, Vim and Emacs, basic and advanced GDB features, permissions, working with the file system, revision control, Unix utilities, environment customization, and using Python for shell scripts. Topics may be added, given sufficient interest. Course website: http://cs1u.stanford.edu

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.

This course will prepare students to interview for software engineering and related internships and full-time positions in industry. Drawing on multiple sources of actual interview questions, students will learn key problem-solving strategies specific to the technical/coding interview. Students will be encouraged to synthesize information they have learned across different courses in the major. Emphasis will be on the oral and combination written-oral modes of communication common in coding interviews, but which are unfamiliar settings for problem solving for many students. Prerequisites: CS 106B or X.

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, and will include tangents that explore sister fields such as augmented reality and 360 video. Students will work in groups to present a final project in building an application for the Oculus Go headset. Enrollment is limited and by rolling application only. Prerequisite: CS 106A or equivalent.

In the last decade, there have been two intersecting trends of major significance to human reproduction. On the one hand, the rapidly declining cost of sequencing human genomes has enabled an explosion of information about polygenic complex diseases as reported in ever larger GWA studies. In parallel, new (statistical, computational, and wetlab) techniques in single cell sequencing technology have emerged that enable high resolution sequence information in ever smaller sample sizes - including embryos. It is now technically and financially feasible to rank-order preimplantation embryos according to polygenic disease risk. This course brings together experts from the computer science, statistical, wet lab, and clinical domains to discuss opportunities to reduce the risk of disease in human embryos, and expand access to IVF to a broader population. Prerequisites: A desire to develop (and deploy) technology in the fertility space. Either: (1) a strong programming and/or math background or (2) a strong biology/clinical background.

William Janeway describes the relationship between technological development, capital markets, and the government as a three-player game. Scientists and entrepreneurs develop breakthrough innovations, aided and amplified by financial capital. Meanwhile, the government serves to either subsidize (as in wartime) or stymie (through regulations) technological development. Often, the advances in economic and military might due to technological advances lead to conflicts between competing countries, whether Japan and the U.S. in the 1970s or China and the U.S. today. Within societies, technological innovation drives outcomes like increased life expectancy, wealth inequality, and in rare cases changes to paradigms of daily life. In this discussion-driven course, we will explore the ripple effects that technological developments have had and will continue to have on the geopolitical world stage, focusing on trends we as computer scientists are uniquely positioned to understand and predict the ramifications of. Prerequisites: The following are not required but will facilitate understanding of the topics covered: computer systems ( CS 110+), artificial intelligence ( CS 221, CS 231N, CS 229, or CS 230), and theory ( CS 161, cryptography).

This seminar assesses the impact of education technologies on learners, teachers, and education systems. Through weekly case studies of ed tech ventures, students will experience and evaluate popular education technologies such as VR,npersonalized learning, makerspaces, and MOOCs. Additionally, students will develop a toolkit of concepts including critical pedagogy, constructivism, behaviorism, and social reconstructionism which they can use to assess education technologies and their personal contributions to the field. This course will focus largely on ventures in the U.S., but the frameworks developed will be applicable to equity and access issues in education throughout the world.

Students will learn about and apply cutting-edge artificial intelligence techniques to real-world social good spaces (such as healthcare, government, education, and environment). Taught jointly by CS+Social Good and the Stanford AI Group, the aim of the class is to empower students to apply these techniques outside of the classroom. The class will focus on techniques from machine learning and deep learning, including regression, support vector machines (SVMs), neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). The course alternates between lectures on machine learning theory and discussions with invited speakers, who will challenge students to apply techniques in their social good domains. Students complete weekly coding assignments reinforcing machine learning concepts and applications. Prerequisites: programming experience at the level of CS107, mathematical fluency at the level of CS103, comfort with probability at the level of CS109 (or equivalent). Application required for enrollment.