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101 - 110 of 134 results for: all courses

PHIL 166: Probability: Ten Great Ideas About Chance (PHIL 266, STATS 167, STATS 267)

Foundational approaches to thinking about chance in matters such as gambling, the law, and everyday affairs. Topics include: chance and decisions; the mathematics of chance; frequencies, symmetry, and chance; Bayes great idea; chance and psychology; misuses of chance; and harnessing chance. Emphasis is on the philosophical underpinnings and problems. Prerequisite: exposure to probability or a first course in statistics at the level of STATS 60 or 116.
Terms: not given this year | Units: 4 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR | Grading: Letter or Credit/No Credit

PHYSICS 50: Observational Astronomy Laboratory

Introduction to observational astronomy emphasizing the use of optical telescopes. Observations of stars, nebulae, and galaxies in laboratory sessions with telescopes at the Stanford Student Observatory. Meets at the observatory one evening per week from dusk until well after dark, in addition to day-time lectures each week. No previous physics required. Limited enrollment.
Terms: Aut, Sum | Units: 3 | UG Reqs: GER: DB-NatSci, WAY-AQR, WAY-SMA | Grading: Letter (ABCD/NP)

PHYSICS 100: Introduction to Observational Astrophysics

Designed for undergraduate physics majors but open to all students with a calculus-based physics background and some laboratory and coding experience. Students make and analyze observations using the telescopes at the Stanford Student Observatory. Topics covered include navigating the night sky, the physics of stars and galaxies, telescope instrumentation and operation, imaging and spectroscopic techniques, quantitative error analysis, and effective scientific communication. The course concludes with an independent project where student teams propose and execute an observational astronomy project of their choosing, using techniques learned in class to gather and analyze their data, and presenting their findings in the forms of professional-style oral presentations and research papers. Enrollment by permission. To get a permission number please complete form: http://web.stanford.edu/~elva/physics100prelim.fbn If you have not heard from us by the beginning of class, please come to the first class session.
Terms: Spr | Units: 4 | UG Reqs: GER: DB-NatSci, WAY-AQR, WAY-SMA | Grading: Letter (ABCD/NP)
Instructors: Allen, S. (PI)

PHYSICS 105: Intermediate Physics Laboratory I: Analog Electronics

Analog electronics including Ohm's law, passive circuits and transistor and op amp circuits, emphasizing practical circuit design skills to prepare undergraduates for laboratory research. Short design project. Minimal use of math and physics, no electronics experience assumed beyond introductory physics. Prerequisite: PHYSICS 43 or PHYSICS 63.
Terms: Aut | Units: 4 | UG Reqs: GER: DB-NatSci, WAY-AQR, WAY-SMA | Grading: Letter or Credit/No Credit

PHYSICS 107: Intermediate Physics Laboratory II: Experimental Techniques and Data Analysis

Experiments on lasers, Gaussian optics, and atom-light interaction, with emphasis on data and error analysis techniques. Students describe a subset of experiments in scientific paper format. Prerequisites: completion of PHYSICS 40 or PHYSICS 60 series, and PHYSICS 70 and PHYSICS 105. Recommended pre- or corequisites: PHYSICS 120 and 130. WIM
Terms: Win | Units: 4 | UG Reqs: WAY-AQR, WAY-SMA | Grading: Letter or Credit/No Credit
Instructors: Hollberg, L. (PI)

PHYSICS 108: Advanced Physics Laboratory: Project

Small student groups plan, design, build, and carry out a single experimental project in low-temperature physics. Prerequisites PHYSICS 105, PHYSICS 107.
Terms: Win, Spr | Units: 4 | UG Reqs: WAY-AQR, WAY-SMA | Grading: Letter or Credit/No Credit

PHYSICS 113: Computational Physics

Numerical methods for solving problems in mechanics, astrophysics, electromagnetism, quantum mechanics, and statistical mechanics. Methods include numerical integration; solutions of ordinary and partial differential equations; solutions of the diffusion equation, Laplace's equation and Poisson's equation with various methods; statistical methods including Monte Carlo techniques; matrix methods and eigenvalue problems. Short introduction to Python, used for class examples; class projects may be programmed in any language such as C, python or julia. No Prerequisites. Previous programming experience not required.
Terms: Spr | Units: 4 | UG Reqs: GER: DB-NatSci, WAY-AQR, WAY-FR | Grading: Letter or Credit/No Credit
Instructors: Cabrera, B. (PI)

POLISCI 101: Introduction to International Relations

Approaches to the study of conflict and cooperation in world affairs. Applications to First and Second World Wars, the Cold War, terrorism, economic policy, and development.
Terms: Aut, Spr | Units: 5 | UG Reqs: GER:DB-SocSci, WAY-AQR, WAY-SI | Grading: Letter or Credit/No Credit

POLISCI 104: Introduction to Comparative Politics

(Formerly POLISCI 4) Why are some countries prone to civil war and violence, while others remain peaceful? Why do some countries maintain democratic systems, while others do not? Why are some countries more prosperous than others? This course will provide an overview of the most basic questions in the comparative study of political systems, and will introduce the analytical tools that can help us answer them.
Terms: Spr | Units: 5 | UG Reqs: GER:DB-SocSci, GER:EC-GlobalCom, WAY-AQR, WAY-SI | Grading: Letter or Credit/No Credit

POLISCI 150A: Data Science for Politics (POLISCI 355A)

Data science is quickly changing the way we understand and and engage in the political process. In this course we will develop fundamental techniques of data science and apply them to large political datasets on elections, campaign finance, lobbying, and more. The objective is to give students the skills to carry out cutting edge quantitative political studies in both academia and the private sector. Students with technical backgrounds looking to study politics quantitatively are encouraged to enroll.
Terms: Aut | Units: 5 | UG Reqs: WAY-AQR | Grading: Letter (ABCD/NP)
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