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in Computer Science, Computer Networks, Thesis Track
Faculty, undergraduates, and graduate students interested in teaching discuss topics raised by teaching computer science at the introductory level. Prerequisite: consent of instructor.
Restriction: Computer Engineering Majors have first consideration for enrollment. Electrical Engineering Majors have first consideration for enrollment. Materials Science Engineering Majors have first consideration for enrollment.
in Computer Science, Non-Thesis Track
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.
This course introduces students to various intersections of social good and technology through a weekly discussion and speaker series. Students will be given a space to exchange ideas and experiences regarding a certain social issue. Invited speakers come from industry, academia, and non-profit organizations. They will share their career paths, what drove them to these fields, and advice for students. The topics examined will span a broad variety of social issues -- from race and class to education and sustainability -- and help students better understand how to kick off their journey in using computer science for social good.
in Computer Science, Thesis Track
Introduction to the fundamental concepts of computer systems through bare metal programming on the Raspberry Pi. Explores how five concepts come together in computer systems: hardware, architecture, assembly code, the C language, and software development tools. Students do all programming with a Raspberry Pi kit and several add-ons (LEDs, buttons). Topics covered include: the C programming language, data representation, machine-level code, computer arithmetic, compilation, memory organization and management, debugging, hardware, and I/O. Prerequisite: 106B or X, and consent of instructor. There is a $50 required lab fee.
Topics include: counting and combinatorics, random variables, conditional probability, independence, distributions, expectation, point estimation, and limit theorems. Applications of probability in computer science including machine learning and the use of probability in the analysis of algorithms. Prerequisites: 103, 106B or X, multivariate calculus at the level of or or equivalent.
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In this increasingly competitive job market, it can be especially helpful to have a degree in your field of interest, and computer science is no different. Even if you are working online from home and don’t have to see your employer often, they will require you to have the skills to do the job well. There are many accredited programs for earning a computer science degree online, allowing you to work at your own pace and get through a program quickly and painlessly.
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