## APPPHYS 100B: The Questions of Cloth: Weaving, Pattern Complexity and Structures of Fabric (ARTSINST 100B)

Students will learn to weave on a table loom while examining textile structures from historic, artistic and scientific perspectives. Emphasis on analyzing patterns and structures generated by weaving, with elementary introductions to information-scientific notions of algorithmic complexity, image compression, and source coding. This class is primarily intended for non-STEM majors with little or no prior experience in working with textiles. Limited enrollment. Prerequisites: Instructor permission.

Last offered: Winter 2023
| UG Reqs: WAY-FR

## ARTSINST 100B: The Questions of Cloth: Weaving, Pattern Complexity and Structures of Fabric (APPPHYS 100B)

Students will learn to weave on a table loom while examining textile structures from historic, artistic and scientific perspectives. Emphasis on analyzing patterns and structures generated by weaving, with elementary introductions to information-scientific notions of algorithmic complexity, image compression, and source coding. This class is primarily intended for non-STEM majors with little or no prior experience in working with textiles. Limited enrollment. Prerequisites: Instructor permission.

Last offered: Winter 2023
| UG Reqs: WAY-FR

## BIO 143: Quantitative Methods for Marine Ecology and Conservation (BIO 243, CEE 164, CEE 264H, EARTHSYS 143H, EARTHSYS 243H, OCEANS 143)

NOTE: This course will be taught in-person on main campus, in hybrid format with Zoom options. The goal of this course is to learn the foundations of ecological modeling with a specific (but not exclusive) focus on marine conservation and sustainable exploitation of renewable resources. Students will be introduced to a range of methods - from basic to advanced - to characterize population structure, conduct demographic analyses, estimate extinction risk, identify temporal trends and spatial patterns, quantify the effect of environmental determinants and anthropogenic pressures on the dynamics of marine populations, describe the potential for adaptation to climate change. This course will emphasize learning by doing, and will rely heavily on practical computer laboratories, in R and/or Phyton, based on data from our own research activities or peer reviewed publications. Students with a background knowledge of statistics, programming and calculus will be most welcome. Formally
BIOHOPK 143H and 243H.

Terms: Win
| Units: 4
| UG Reqs: WAY-AQR, WAY-FR

Instructors:
De Leo, G. (PI)
;
Friedman, K. (TA)

## BIODS 48N: Riding the Data Wave (STATS 48N)

Imagine collecting a bit of your saliva and sending it in to one of the personalized genomics company: for very little money you will get back information about hundreds of thousands of variable sites in your genome. Records of exposure to a variety of chemicals in the areas you have lived are only a few clicks away on the web; as are thousands of studies and informal reports on the effects of different diets, to which you can compare your own. What does this all mean for you? Never before in history humans have recorded so much information about themselves and the world that surrounds them. Nor has this data been so readily available to the lay person. Expression as "data deluge'' are used to describe such wealth as well as the loss of proper bearings that it often generates. How to summarize all this information in a useful way? How to boil down millions of numbers to just a meaningful few? How to convey the gist of the story in a picture without misleading oversimplifications? To an
more »

Imagine collecting a bit of your saliva and sending it in to one of the personalized genomics company: for very little money you will get back information about hundreds of thousands of variable sites in your genome. Records of exposure to a variety of chemicals in the areas you have lived are only a few clicks away on the web; as are thousands of studies and informal reports on the effects of different diets, to which you can compare your own. What does this all mean for you? Never before in history humans have recorded so much information about themselves and the world that surrounds them. Nor has this data been so readily available to the lay person. Expression as "data deluge'' are used to describe such wealth as well as the loss of proper bearings that it often generates. How to summarize all this information in a useful way? How to boil down millions of numbers to just a meaningful few? How to convey the gist of the story in a picture without misleading oversimplifications? To answer these questions we need to consider the use of the data, appreciate the diversity that they represent, and understand how people instinctively interpret numbers and pictures. During each week, we will consider a different data set to be summarized with a different goal. We will review analysis of similar problems carried out in the past and explore if and how the same tools can be useful today. We will pay attention to contemporary media (newspapers, blogs, etc.) to identify settings similar to the ones we are examining and critique the displays and summaries there documented. Taking an experimental approach, we will evaluate the effectiveness of different data summaries in conveying the desired information by testing them on subsets of the enrolled students.

Last offered: Autumn 2020
| UG Reqs: WAY-AQR, WAY-FR

## BIOE 80: Introduction to Bioengineering (Engineering Living Matter) (ENGR 80)

Students completing
BIOE 80 should have a working understanding for how to approach the systematic engineering of living systems to benefit all people and the planet. Our main goals are (1) to help students learn ways of thinking about engineering living matter and (2) to empower students to explore the broader ramifications of engineering life. Specific concepts and skills covered include but are not limited to: capacities of natural life on Earth; scope of the existing human-directed bioeconomy; deconstructing complicated problems; reaction & diffusion systems; microbial human anatomy; conceptualizing the engineering of biology; how atoms can be organized to make molecules; how to print DNA from scratch; programming genetic sensors, logic, & actuators; biology beyond molecules (photons, electrons, etc.); constraints limiting what life can do; and possible health challenges in 2030. And we explore questions like, how does what we want shape bioengineering, and who should choose and realize various competing bioengineering futures?

Terms: Spr
| Units: 4
| UG Reqs: WAY-FR, GER:DB-EngrAppSci

Instructors:
Brophy, J. (PI)
;
Endy, D. (PI)
;
Espinoza Campomanes, L. (TA)
...
more instructors for BIOE 80 »

Instructors:
Brophy, J. (PI)
;
Endy, D. (PI)
;
Espinoza Campomanes, L. (TA)
;
Flores Perez, A. (TA)
;
Haddad, E. (TA)
;
Moreno Carbonell, L. (TA)
;
Shi, S. (TA)
;
Steele, S. (TA)

## CEE 164: Quantitative Methods for Marine Ecology and Conservation (BIO 143, BIO 243, CEE 264H, EARTHSYS 143H, EARTHSYS 243H, OCEANS 143)

NOTE: This course will be taught in-person on main campus, in hybrid format with Zoom options. The goal of this course is to learn the foundations of ecological modeling with a specific (but not exclusive) focus on marine conservation and sustainable exploitation of renewable resources. Students will be introduced to a range of methods - from basic to advanced - to characterize population structure, conduct demographic analyses, estimate extinction risk, identify temporal trends and spatial patterns, quantify the effect of environmental determinants and anthropogenic pressures on the dynamics of marine populations, describe the potential for adaptation to climate change. This course will emphasize learning by doing, and will rely heavily on practical computer laboratories, in R and/or Phyton, based on data from our own research activities or peer reviewed publications. Students with a background knowledge of statistics, programming and calculus will be most welcome. Formally
BIOHOPK 143H and 243H.

Terms: Win
| Units: 4
| UG Reqs: WAY-AQR, WAY-FR

Instructors:
De Leo, G. (PI)
;
Friedman, K. (TA)

## CME 100: Vector Calculus for Engineers (ENGR 154)

Computation and visualization using MATLAB. Differential vector calculus: vector-valued functions, analytic geometry in space, functions of several variables, partial derivatives, gradient, linearization, unconstrained maxima and minima, Lagrange multipliers and applications to trajectory simulation, least squares, and numerical optimization. Introduction to linear algebra: matrix operations, systems of algebraic equations with applications to coordinate transformations and equilibrium problems. Integral vector calculus: multiple integrals in Cartesian, cylindrical, and spherical coordinates, line integrals, scalar potential, surface integrals, Green's, divergence, and Stokes' theorems. Numerous examples and applications drawn from classical mechanics, fluid dynamics and electromagnetism. Prerequisites: knowledge of single-variable calculus equivalent to the content of
Math 19-21 (e.g., 5 on Calc BC, 4 on Calc BC with
Math 21, 5 on Calc AB with
Math 21). Placement diagnostic (recommendation non-binding) at:
https://exploredegrees.stanford.edu/undergraduatedegreesandprograms/#aptext.

Terms: Aut, Spr
| Units: 5
| UG Reqs: WAY-FR, GER:DB-Math

Instructors:
Ankeney, G. (PI)
;
Ayala Bellido, C. (PI)
;
Khayms, V. (PI)
...
more instructors for CME 100 »

Instructors:
Ankeney, G. (PI)
;
Ayala Bellido, C. (PI)
;
Khayms, V. (PI)
;
Le, H. (PI)
;
Ankeney, G. (TA)
;
Du, T. (TA)
;
Garg, R. (TA)
;
Kopff, A. (TA)
;
Lan, H. (TA)
;
Muneton Gallego, J. (TA)
;
Nzia Yotchoum, H. (TA)
;
Ramanantsoa, R. (TA)
;
Xu, B. (TA)

## CME 102: Ordinary Differential Equations for Engineers (ENGR 155A)

Analytical and numerical methods for solving ordinary differential equations arising in engineering applications are presented. For analytical methods students learn to solve linear and non-linear first order ODEs; linear second order ODEs; and Laplace transforms. Numerical methods using MATLAB programming tool kit are also introduced to solve various types of ODEs including: first and second order ODEs, higher order ODEs, systems of ODEs, initial and boundary value problems, finite differences, and multi-step methods. This also includes accuracy and linear stability analyses of various numerical algorithms which are essential tools for the modern engineer. This class is foundational for professional careers in engineering and as a preparation for more advanced classes at the undergraduate and graduate levels. Prerequisites: knowledge of single-variable calculus equivalent to the content of
Math 19-21 (e.g., 5 on Calc BC, 4 on Calc BC with
Math 21, 5 on Calc AB with
Math 21). Placement diagnostic (recommendation non-binding) at:
https://exploredegrees.stanford.edu/undergraduatedegreesandprograms/#aptext.

Terms: Aut, Win
| Units: 5
| UG Reqs: GER:DB-Math, WAY-FR

Instructors:
Darve, E. (PI)
;
Le, H. (PI)
;
Ankeney, G. (TA)
;
Ayala Bellido, C. (TA)
;
Chen, C. (TA)
;
Diller, E. (TA)
;
Nzia Yotchoum, H. (TA)

## CME 104: Linear Algebra and Partial Differential Equations for Engineers (ENGR 155B)

Linear algebra: systems of algebraic equations, Gaussian elimination, undetermined and overdetermined systems, coupled systems of ordinary differential equations, LU factorization, eigensystem analysis, normal modes. Linear independence, vector spaces, subspaces and basis. Numerical analysis applied to structural equilibrium problems, electrical networks, and dynamic systems. Fourier series with applications, partial differential equations arising in science and engineering, analytical solutions of partial differential equations. Applications in heat and mass transport, mechanical vibration and acoustic waves, transmission lines, and fluid mechanics. Numerical methods for solution of partial differential equations: iterative techniques, stability and convergence, time advancement, implicit methods, von Neumann stability analysis. Examples and applications drawn from a variety of engineering fields. Prerequisite:
CME102/
ENGR155A.

Terms: Spr
| Units: 5
| UG Reqs: GER:DB-Math, WAY-FR

## CME 106: Introduction to Probability and Statistics for Engineers (ENGR 155C)

Probability: random variables, independence, and conditional probability; discrete and continuous distributions, moments, distributions of several random variables. Numerical simulation using Monte Carlo techniques. Topics in mathematical statistics: random sampling, point estimation, confidence intervals, hypothesis testing, non-parametric tests, regression and correlation analyses. Numerous applications in engineering, manufacturing, reliability and quality assurance, medicine, biology, and other fields. Prerequisite:
CME100/ENGR154 or
Math 51 or 52.

Terms: Win, Sum
| Units: 4
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

Instructors:
Khayms, V. (PI)
;
Muneton Gallego, J. (PI)
;
Ramanantsoa, R. (PI)
...
more instructors for CME 106 »

Instructors:
Khayms, V. (PI)
;
Muneton Gallego, J. (PI)
;
Ramanantsoa, R. (PI)
;
Xu, B. (PI)
;
Xu, B. (TA)

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