2015-2016 2016-2017 2017-2018 2018-2019 2019-2020
Browse
by subject...
    Schedule
view...
 

1 - 10 of 117 results for: CS ; Currently searching spring courses. You can expand your search to include all quarters

CS 1U: Practical Unix

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
Terms: Aut, Win, Spr | Units: 1
Instructors: Zelenski, J. (PI)

CS 11SI: How to Make VR: Introduction to Virtual Reality Design and Development

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. Students will work in groups to present a final project in building an application for the Oculus Go headset. Enrollment is limited and by application only. See https://cs11si.stanford.edu for more information. Prerequisite: CS 106A or equivalent.
Terms: Aut, Win, Spr | Units: 2

CS 21SI: AI for Social Good

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.
Terms: Spr | Units: 2
Instructors: Piech, C. (PI)

CS 49N: Using Bits to Control Atoms

This is a crash course in how to use a stripped-down computer system about the size of a credit card (the rasberry pi computer) to control as many different sensors as we can implement in ten weeks, including LEDs, motion sensors, light controllers, and accelerometers. The ability to fearlessly grab a set of hardware devices, examine the data sheet to see how to use it, and stitch them together using simple code is a secret weapon that software-only people lack, and allows you to build many interesting gadgets. We will start with a "bare metal'' system --- no operating system, no support --- and teach you how to read device data sheets describing sensors and write the minimal code needed to control them (including how to debug when things go wrong, as they always do). This course differs from most in that it is deliberately mostly about what and why rather than how --- our hope is that the things you are able at the end will inspire you to follow the rest of the CS curriculum to understand better how things you've used work. Prerequisites: knowledge of the C programming language. A Linux or Mac laptop that you are comfortable coding on.
Terms: Spr | Units: 3
Instructors: Engler, D. (PI)

CS 52: CS + Social Good Studio: Implementing Social Good Projects

Continuation of CS51 (CS + Social Good Studio). Teams enter the quarter having completed and tested a minimal viable product (MVP) with a well-defined target user, and a community partner. Students will learn to apply scalable technical frameworks, methods to measure social impact, tools for deployment, user acquisition techniques and growth/exit strategies. The purpose of the class is to facilitate students to build a sustainable infrastructure around their product idea. CS52 will host mentors, guest speakers and industry experts for various workshops and coaching-sessions. The class culminates in a showcase where students share their projects with stakeholders and the public. Prerequisite: CS 51, or consent of instructor.
Terms: Spr | Units: 2
Instructors: Cain, J. (PI)

CS 81SI: AI Interpretability and Fairness

As black-box AI models grow increasingly relevant in human-centric applications, explainability and fairness becomes increasingly necessary for trust in adopting AI models. This seminar class introduces students to major problems in AI explainability and fairness, and explores key state-of-theart methods. Key technical topics include surrogate methods, feature visualization, network dissection, adversarial debiasing, and fairness metrics. There will be a survey of recent legal and policy trends. Each week a guest lecturer from AI research, industry, and related policy fields will present an open problem and solution, followed by a roundtable discussion with the class. Students have the opportunity to present a topic of interestnor application to their own projects (solo or in teams) in the final class. Code examples of each topic will be provided for students interested in a particular topic, but there will be no required coding components. Students who will benefit most from this clas more »
As black-box AI models grow increasingly relevant in human-centric applications, explainability and fairness becomes increasingly necessary for trust in adopting AI models. This seminar class introduces students to major problems in AI explainability and fairness, and explores key state-of-theart methods. Key technical topics include surrogate methods, feature visualization, network dissection, adversarial debiasing, and fairness metrics. There will be a survey of recent legal and policy trends. Each week a guest lecturer from AI research, industry, and related policy fields will present an open problem and solution, followed by a roundtable discussion with the class. Students have the opportunity to present a topic of interestnor application to their own projects (solo or in teams) in the final class. Code examples of each topic will be provided for students interested in a particular topic, but there will be no required coding components. Students who will benefit most from this class have exposure to AI, such as through projects and related coursework (e.g. statistics, CS221, CS230, CS229). Students who are pursuing subjects outside of the CS department (e.g. sciences, social sciences, humanities) with sufficient mathematical maturity are welcomed to apply. Enrollment limited to 20.
Terms: Spr | Units: 1

CS 82SI: Wellness in Tech: Designing an Intentional Lifestyle in a Tech-Driven World

Would deleting Facebook make us all happier? Of the 16 hours we spend awake each day on average, over 11 of those hours are spent interacting with digital media. In an always-on, tech-driven world, how do we regain control over our wellbeing?nThis 1 unit course is part workshop, part seminar, with a focus on tackling and re-framing the relationship between technology and wellness. What are the principles of human flourishing, and what is technology's role in promoting them? How can self-compassion and an appreciation for diversity lead to the development of products that enhance our collective happiness? Using human-centered design thinking, we will explore how technology both propels and hinders us- as individuals and as a society. By the end of this course, you will have tangible insights and methods to regain control over your relationship with technology. No coding involved; however we will be deeply exploring the human operating system. Students from all programs and areas of study are encouraged to apply.
Terms: Spr | Units: 1
Instructors: Piech, C. (PI)

CS 84: Emotional Intelligence

This hands-on course is aimed at Stanford engineers who wish to be successful in start-ups or engineering-focused organizations. It is based on decades of observations by the instructors, witnessing that fresh graduates routinely struggle to survive and create an impact in the corporate world. A key objective is for students to develop a basic set of skills to master day-to-day personal interactions, and to understand the dynamics of work environments. The course then aims to guide students with more complex tasks, such as how to run effective meetings or how to work in multi-disciplinary teams. Whether you wish to become a start-up founder and CEO; a manager at a tech-centric company; or an individual contributor at Facebook or Google: if you wish to hit the ground running and be highly effective from your first day at work, this course is for you!
Terms: Spr | Units: 2
Instructors: Thrun, S. (PI)

CS 100A: Problem-solving Lab for CS106A

Additional problem solving practice for the introductory CS course CS 106A. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Limited enrollment, permission of instructor required. Concurrent enrollment in CS 106A required.
Terms: Aut, Win, Spr | Units: 1

CS 100B: Problem-solving Lab for CS106B

Additional problem solving practice for the introductory CS course CS106B. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Limited enrollment, permission of instructor required. Concurrent enrollment in CS 106B required.
Terms: Aut, Win, Spr | Units: 1
Filter Results:
term offered
updating results...
number of units
updating results...
time offered
updating results...
days
updating results...
UG Requirements (GERs)
updating results...
component
updating results...
career
updating results...
© Stanford University | Terms of Use | Copyright Complaints