2013-2014 2014-2015 2015-2016 2016-2017 2017-2018
Browse
by subject...
    Schedule
view...
 

1 - 10 of 86 results for: CS

CS 1C: Introduction to Computing at Stanford

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.
Terms: Aut | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Smith, S. (PI)

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. The time listed on AXESS is for the first week's logistical meeting only. 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, Spr | Units: 1 | Grading: Satisfactory/No Credit

CS 2C: Introduction to Media Production

Sound, image and video editing techniques and applications, best practices and information regarding Stanford media support. Technical topics will cover Photoshop, iMovie and Garageband. Weekly pre-class online tutorials followed by weekly group work and peer critiques. Not a programming course, but will use computer multimedia applications heavily for editing.
Terms: Aut, Win | Units: 2 | Grading: Satisfactory/No Credit
Instructors: Scott, E. (PI)

CS 9: Problem-Solving for the CS Technical Interview

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.
Terms: Aut | Units: 1 | Grading: Satisfactory/No Credit

CS 44N: Computational Thinking and Systems in the Real-World

Computing in the real-world is too often viewed as working away concocting some computer incantations hidden inside some high technology company. However, computing and computer communication has infiltrated and in many cases revolutionized several ¿systems¿ in the real world, including financial systems, inventory management, advertising systems, supply chain management, transportation systems, defense systems and so on. Moreover, the discipline of thinking that has developed to build these systems, computational thinking, has powerful applicability to real-world problems and situations outside of computer programming. This course provides an introduction and exposure to some of these dramatic trends, opportunities and risks. Also included is an introduction to some basic ideas in ¿computational thinking¿. The course will include guest speakers. No programming competence is assumed but exposure to programming would be useful. Interest in the real world and interest is not being run-over by this trend is essential.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Cheriton, D. (PI)

CS 45N: Computers and Photography: From Capture to Sharing

Preference to freshmen with experience in photography and use of computers. Elements of photography, such as lighting, focus, depth of field, aperture, and composition. How a photographer makes photos available for computer viewing, reliably stores them, organizes them, tags them, searches them, and distributes them online. No programming experience required. Digital SLRs and editing software will be provided to those students who do not wish to use their own.
Terms: Aut | Units: 3-4 | UG Reqs: WAY-CE | Grading: Letter or Credit/No Credit

CS 46N: Big Data, Big Discoveries, Big Fallacies

A sea change has occurred in science, technology, medicine, politics, and society as a whole: many of the world's biggest discoveries and decisions are now being made on the basis of analyzing massive data sets, referred to as "big data". Everyday examples include social-network friend recommendations, and weather predictions far more accurate than a decade ago; both use vast collections of data to model the past and predict the future. But it is surprisingly easy to come to false conclusions from data analysis alone. For example, we might conclude that acne medicine prevents heart attacks and strokes, if we forget to factor in the age of the patients. Privacy is a major concern: Target stores analyzed buying patterns to predict with remarkable accuracy which of their shoppers had just become pregnant, but trouble arose when they sent baby ads to the homes of pregnant teens whose parents weren't yet in the know. We will start by surveying the history of data-driven activities, leading up to the recent Big Data explosion. A variety of data analysis techniques will be covered, leading students to appreciate that even simple techniques can go a long way when the data set is large enough. Common stumbling blocks leading to false conclusions will be discussed, and students will be asked to debate the many issues surrounding privacy. In one project, students will see whose analysis techniques can best predict user movie ratings based on past rating behavior. A second project will be individually designed in an area of the student's choosing. The seminar will include a mix of assigned readings, small-scale investigations and assignments, classroom discussions, and two projects. No computer programming or special math skills are required; students will learn the basic techniques and tools they need to complete the data analysis assignments and projects.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit

CS 54N: Great Ideas in Computer Science

Stanford Introductory Seminar. Preference to freshmen. Covers the intellectual tradition of computer science emphasizing ideas that reflect the most important milestones in the history of the discipline. No prior experience with programming is assumed. Topics include programming and problem solving; implementing computation in hardware; algorithmic efficiency; the theoretical limits of computation; cryptography and security; and the philosophy behind artificial intelligence.
Terms: Aut | Units: 3 | UG Reqs: GER:DB-EngrAppSci | Grading: Letter or Credit/No Credit
Instructors: Roberts, E. (PI)

CS 90SI: CS + Social Good: Using Web Technologies to Change the World

Learn web technologies by working on real world projects focused on creating positive social impact. The class will cover basic topics related to web development and provide resources for more advanced learning. Students will work on small teams to implement high-impact projects for partner organizations. The aim of the class is to empower students to leverage technology for social good by inspiring action, facilitating collaboration, and forging pathways toward change. No web application experience required. Prerequisite: 106B. Application required; apply online at http://bit.ly/90siApp. Applications accepted until midnight on September 14th.
Terms: Aut | Units: 2 | Grading: Satisfactory/No Credit
Instructors: Cain, J. (PI)

CS 92SI: Hap.py Coder: The Python Programming Language

The fundamentals and contemporary usage of the Python programming language. Primary focus on developing best practices in writing Python and exploring the extensible and unique parts of Python that make it such a powerful scripting language. Topics include: data structures (e.g. lists and dictionaries), and characteristic pythonic conventions like anonymous functions, iterables, and powerful built-ins (e.g. map, filter, zip). Time permitting, we will also cover object-oriented design, modules, (e.g. request, itertools), and modern Python-based web frameworks. Prerequisite: 106A. Application required.
Terms: Aut | Units: 2 | Grading: Satisfactory/No Credit
Instructors: Parlante, N. (PI)
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