printer friendly pageMATSCI 190: Organic and Biological Materials (MATSCI 210)
Unique physical and chemical properties of organic materials and their uses. The relationship between structure and physical properties, and techniques to determine chemical structure and molecular ordering. Examples include liquid crystals, dendrimers, carbon nanotubes, hydrogels, and biopolymers such as lipids, protein, and DNA. Prerequisite: Thermodynamics and
ENGR 50 or equivalent. Undergraduates register for 190 for 4 units; graduates register for 210 for 3 units.
Terms: Spr
| Units: 3-4
| UG Reqs: WAY-SMA, WAY-AQR, GER:DB-EngrAppSci
ME 1: Introduction to Mechanical Engineering
This course is intended to be the starting point for Mechanical Engineering majors. It will cover the concepts, engineering methods, and common tools used by mechanical engineers while introducing the students to a few interesting devices. We will discuss how each device was conceived, design challenges that arose, application of analytical tools to the design, and production methods. Main class sections will include lectures, demonstrations, and in-class group exercises. Lab sections will develop specific skills in freehand sketching and computational modeling of engineering systems. Prerequisites: Physics: Mechanics, and first quarter Calculus.
Terms: Aut, Spr
| Units: 3
| UG Reqs: WAY-AQR
Instructors:
Su, L. (PI)
;
Amador, J. (TA)
;
Brown, J. (TA)
;
Robertson, T. (TA)
;
Robinson, K. (TA)
;
Schweizer, E. (TA)
ME 30: Engineering Thermodynamics
The basic principles of thermodynamics are introduced in this course. Concepts of energy and entropy from elementary considerations of the microscopic nature of matter are discussed. The principles are applied in thermodynamic analyses directed towards understanding the performances of engineering systems. Methods and problems cover socially responsible economic generation and utilization of energy in central power generation plants, solar systems, refrigeration devices, and automobile, jet and gas-turbine engines.
Terms: Aut, Win, Spr
| Units: 3
| UG Reqs: WAY-AQR, WAY-SMA
Instructors:
Edwards, M. (PI)
;
Ihme, M. (PI)
;
Wang, H. (PI)
;
Krishnamoorti, A. (TA)
;
Lietch, E. (TA)
;
Min, O. (TA)
;
Muhunthan, P. (TA)
;
Robertson, T. (TA)
;
Younes, K. (TA)
ME 80: Mechanics of Materials
Mechanics of materials and deformation of structural members. Topics include stress and deformation analysis under axial loading, torsion and bending, column buckling and pressure vessels. Introduction to stress transformation and multiaxial loading. Prerequisite:
ENGR 14.
Terms: Aut, Win
| Units: 3
| UG Reqs: GER:DB-EngrAppSci, WAY-AQR
Instructors:
Levenston, M. (PI)
;
Zhao, R. (PI)
;
Amador, J. (TA)
;
Anderson, C. (TA)
;
Bernardo, J. (TA)
;
Robinson, K. (TA)
MS&E 111: Introduction to Optimization (ENGR 62, MS&E 211)
Formulation and computational analysis of linear, quadratic, and other convex optimization problems. Applications in machine learning, operations, marketing, finance, and economics. Prerequisite:
CME 100 or
MATH 51.
Terms: Aut
| Units: 4
| UG Reqs: GER:DB-EngrAppSci, WAY-AQR
Instructors:
Ye, Y. (PI)
;
Herrscher, J. (TA)
;
Hu, C. (TA)
;
Sun, C. (TA)
;
Zhang, E. (TA)
;
Zhang, X. (TA)
MS&E 111X: Introduction to Optimization (Accelerated) (MS&E 211X)
Optimization theory and modeling. The role of prices, duality, optimality conditions, and algorithms in finding and recognizing solutions. Perspectives: problem formulation, analytical theory, computational methods, and recent applications in engineering, finance, and economics. Theories: finite dimensional derivatives, convexity, optimality, duality, and sensitivity. Methods: simplex and interior-point, gradient, Newton, and barrier. Prerequisite:
CME 100 or
MATH 51 or equivalent.
Terms: Spr
| Units: 4
| UG Reqs: WAY-AQR
MS&E 120: Introduction to Probability
Probability is the foundation behind many important disciplines including statistics, machine learning, risk analysis, stochastic modeling and optimization. This course provides an in-depth undergraduate-level introduction to fundamental ideas and tools of probability. Topics include: the foundations (sample spaces, random variables, probability distributions, conditioning, independence, expectation, variance), a systematic study of the most important univariate and multivariate distributions (Normal, Multivariate Normal, Binomial, Poisson, etc...), as well as a peek at some limit theorems (basic law of large numbers and central limit theorem) and, time permitting, some elementary markov chain theory. Prerequisite:
CME 100 or
MATH 51.
Terms: Aut
| Units: 4
| UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR
MS&E 134: Solving Social Problems with Data (COMM 140X, DATASCI 154, EARTHSYS 153, ECON 163, POLISCI 154, PUBLPOL 155, SOC 127)
Introduces students to the interdisciplinary intersection of data science and the social sciences through an in-depth examination of contemporary social problems. Provides a foundational skill set for solving social problems with data including quantitative analysis, modeling approaches from the social sciences and engineering, and coding skills for working directly with big data. Students will also consider the ethical dimensions of working with data and learn strategies for translating quantitative results into actionable policies and recommendations. Lectures will introduce students to the methods of data science and social science and apply these frameworks to critical 21st century challenges, including education & inequality, political polarization, and health equity & algorithmic design in the fall quarter, and social media, climate change, and school choice & segregation in the spring quarter. In-class exercises and problem sets will provide students with the opportunity to use
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Introduces students to the interdisciplinary intersection of data science and the social sciences through an in-depth examination of contemporary social problems. Provides a foundational skill set for solving social problems with data including quantitative analysis, modeling approaches from the social sciences and engineering, and coding skills for working directly with big data. Students will also consider the ethical dimensions of working with data and learn strategies for translating quantitative results into actionable policies and recommendations. Lectures will introduce students to the methods of data science and social science and apply these frameworks to critical 21st century challenges, including education & inequality, political polarization, and health equity & algorithmic design in the fall quarter, and social media, climate change, and school choice & segregation in the spring quarter. In-class exercises and problem sets will provide students with the opportunity to use real-world datasets to discover meaningful insights for policymakers and communities. This course is the required gateway course for the new major in Data Science & Social Systems. Preference given to Data Science & Social Systems B.A. majors and prospective majors. Course material and presentation will be at an introductory level. Enrollment and participation in one discussion section is required. Sign up for the discussion section will occur on Canvas at the start of the quarter. Prerequisites:
CS106A (required),
DATASCI 112 (recommended as pre or corequisite). Limited enrollment. Please complete the interest form here:
https://forms.gle/8ui9RPgzxjGxJ9k29. A permission code will be given to admitted students to register for the class.
Terms: Aut, Spr
| Units: 5
| UG Reqs: WAY-SI, WAY-AQR
Instructors:
Nobles, M. (PI)
;
Weinstein, J. (PI)
;
Castro Pena, D. (TA)
...
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Instructors:
Nobles, M. (PI)
;
Weinstein, J. (PI)
;
Castro Pena, D. (TA)
;
Charmot, S. (TA)
;
Dann, C. (TA)
;
Ma, S. (TA)
;
Ross, D. (TA)
;
Upadhyay, R. (TA)
;
Vasilenok, N. (TA)
MS&E 152: Introduction to Decision Analysis
How to make good decisions in a complex, dynamic, and uncertain world. People often make decisions that on close examination they regard as wrong. Decision analysis uses a structured conversation based on actional thought to obtain clarity of action in a wide variety of domains. Topics: distinctions, possibilities and probabilities, relevance, value of information and experimentation, relevance and decision diagrams, risk attitude. Prerequisites: high school algebra and basic spreadsheet skills.
Terms: Spr
| Units: 3-4
| UG Reqs: WAY-AQR, GER:DB-EngrAppSci, WAY-FR
Instructors:
Shachter, R. (PI)
;
Dai, Y. (TA)
;
Rodriguez Silva Santisteban, F. (TA)
...
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Instructors:
Shachter, R. (PI)
;
Dai, Y. (TA)
;
Rodriguez Silva Santisteban, F. (TA)
;
Wu, Z. (TA)
;
Zhao, Y. (TA)
OCEANS 14H: Bio-logging and Bio-telemetry
Bio-logging is a rapidly growing discipline that includes diverse fields such as consumer electronics, medicine, and marine biology. The use of animal-attached digital tags is a powerful approach to study the movement and ecology of individuals over a wide range of temporal and spatial scales. This course is an introduction to bio-logging methods and analysis. Using whales as a model system, students will learn how use multi-sensor tags to study behavioral biomechanics. Course taught in-person only at Hopkins Marine Station; for information on how to spend spring quarter in residence:
https://hopkinsmarinestation.stanford.edu/undergraduate-studies/spring-courses-23-24 (Individual course registration also permitted.) Depending on enrollment numbers, a weekly shuttle to Hopkins or mileage reimbursements for qualifying carpools will be provided; terms and conditions apply.
Terms: Spr
| Units: 3
| UG Reqs: WAY-AQR, WAY-SMA
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