CME 20Q: Computational Modeling for Future Leaders
Preference to sophomores. How can we harness and exploit the power of computational modeling? What responsibilities are there in developing and using computer models? In this course we will analyze fundamental issues inherent to computational modeling such as uncertainty, predictability, error, and resolution. We will furthermore examine the social context of computational modeling including the public perception of computational models, how computer modeling impacts politics and policy, and how politics and policy, in turn, influence computer modeling.
Terms: Aut

Units: 3

Grading: Letter (ABCD/NP)
Instructors:
Minion, M. (PI)
CME 100: Vector Calculus for Engineers (ENGR 154)
Computation and visualization using MATLAB. Differential vector calculus: analytic geometry in space, functions of several variables, partial derivatives, gradient, unconstrained maxima and minima, Lagrange multipliers. Introduction to linear algebra: matrix operations, systems of algebraic equations, methods of solution and applications. Integral vector calculus: multiple integrals in Cartesian, cylindrical, and spherical coordinates, line integrals, scalar potential, surface integrals, Green¿s, divergence, and Stokes¿ theorems. Examples and applications drawn from various engineering fields. Prerequisites: 10 units of AP credit (Calc BC with 4 or 5, or Calc AB with 5), or
Math 41 and 42.
Terms: Aut, Spr

Units: 5

UG Reqs: GER:DBMath, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Khayms, V. (PI)
;
Le, H. (PI)
;
Aboumrad, G. (TA)
;
Bhargava, P. (TA)
;
DePaul, G. (TA)
;
Fadavi, D. (TA)
;
FournierBidoz, E. (TA)
;
Gao, P. (TA)
;
Genin, M. (TA)
;
Hegde, V. (TA)
;
Krason, M. (TA)
;
Lakshman, V. (TA)
;
Lenain, R. (TA)
;
Sanchez, S. (TA)
;
Storchan, V. (TA)
;
Suresh, S. (TA)
;
Westhoff, P. (TA)
;
Yang, F. (TA)
;
shirian, y. (TA)
CME 100A: Vector Calculus for Engineers, ACE
Students attend
CME100/ENGR154 lectures with additional recitation sessions; two to four hours per week, emphasizing engineering mathematical applications and collaboration methods. Enrollment by department permission only. Prerequisite: must be enrolled in the regular
CME10001 or 02. Application at:
https://engineering.stanford.edu/students/programs/engineeringdiversityprograms/additionalcalculusengineers
Terms: Aut, Spr

Units: 6

UG Reqs: GER:DBMath, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Khayms, V. (PI)
;
Le, H. (PI)
;
Aboumrad, G. (TA)
;
Bhargava, P. (TA)
;
DePaul, G. (TA)
;
Fadavi, D. (TA)
;
FournierBidoz, E. (TA)
;
Gao, P. (TA)
;
Genin, M. (TA)
;
Hegde, V. (TA)
;
Krason, M. (TA)
;
Lakshman, V. (TA)
;
Lenain, R. (TA)
;
Sanchez, S. (TA)
;
Storchan, V. (TA)
;
Suresh, S. (TA)
;
Westhoff, P. (TA)
;
Yang, F. (TA)
;
shirian, y. (TA)
CME 102: Ordinary Differential Equations for Engineers (ENGR 155A)
Analytical and numerical methods for solving ordinary differential equations arising in engineering applications: Solution of initial and boundary value problems, series solutions, Laplace transforms, and nonlinear equations; numerical methods for solving ordinary differential equations, accuracy of numerical methods, linear stability theory, finite differences. Introduction to MATLAB programming as a basic tool kit for computations. Problems from various engineering fields. Prerequisite: 10 units of AP credit (Calc BC with 4 or 5, or Calc AB with 5), or
Math 41 and 42. Recommended:
CME100.
Terms: Aut, Win, Spr, Sum

Units: 5

UG Reqs: GER:DBMath, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Darve, E. (PI)
;
Le, H. (PI)
;
Baalbaki, W. (TA)
;
DePaul, G. (TA)
;
Gnanasekaran, A. (TA)
;
Lorenzetti, J. (TA)
;
Martinez, J. (TA)
;
Moon, T. (TA)
;
Najmabadi, C. (TA)
;
Sanchez, S. (TA)
;
Simpson, C. (TA)
;
Suresh, S. (TA)
;
Westhoff, P. (TA)
CME 102A: Ordinary Differential Equations for Engineers, ACE
Students attend
CME102/ENGR155A lectures with additional recitation sessions; two to four hours per week, emphasizing engineering mathematical applications and collaboration methods. Prerequisite: students must be enrolled in the regular section (
CME102) prior to submitting application at:n
https://engineering.stanford.edu/students/programs/engineeringdiversityprograms/additionalcalculusengineers
Terms: Aut, Win, Spr

Units: 6

UG Reqs: GER:DBMath, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Darve, E. (PI)
;
Le, H. (PI)
;
Baalbaki, W. (TA)
;
Darve, E. (TA)
;
DePaul, G. (TA)
;
Lorenzetti, J. (TA)
;
Lorenzetti, J. (TA)
;
Martinez, J. (TA)
;
Moon, T. (TA)
;
Najmabadi, C. (TA)
;
Sanchez, S. (TA)
;
Simpson, C. (TA)
;
Suresh, S. (TA)
;
Westhoff, P. (TA)
CME 103: Introduction to Matrix Methods (EE 103)
Introduction to applied linear algebra with emphasis on applications. Vectors, norm, and angle; linear independence and orthonormal sets; applications to document analysis. Clustering and the kmeans algorithm. Matrices, left and right inverses, QR factorization. Leastsquares and model fitting, regularization and crossvalidation. Constrained and nonlinear leastsquares. Applications include timeseries prediction, tomography, optimal control, and portfolio optimization. Prerequisites:
MATH 51 or
CME 100, and basic knowledge of computing (
CS 106A is more than enough, and can be taken concurrently).
EE103/CME103 and
Math 104 cover complementary topics in applied linear algebra. The focus of EE103 is on a few linear algebra concepts, and many applications; the focus of
Math 104 is on algorithms and concepts.
Terms: Aut

Units: 35

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Boyd, S. (PI)
;
Angeris, G. (TA)
;
Busseti, E. (TA)
;
Fan, L. (TA)
;
Hwang, J. (TA)
;
Leung, K. (TA)
;
Mu, R. (TA)
;
Nishimura, M. (TA)
;
Park, D. (TA)
;
Pathak, R. (TA)
;
Prasad, V. (TA)
;
Teamangkornpan, P. (TA)
CME 151: Introduction to Data Visualization
Bring your data to life with beautiful and interactive visualizations. This course is designed to provide practical experience on combining data science and graphic design to effectively communicate knowledge buried inside complex data. Each lecture will explore a different set of free industrystandard tools, for example d3.js, three.js, ggplots2, and processing; enabling students to think critically about how to architect their own interactive visualization for data exploration, web, presentations, and publications. Geared towards scientists and engineers, and with a particular emphasis on web, this course assumes an advanced background in programming methodology in multiple languages (particularly R and Javascript). Assignments are short and focus on visual experimentation with interesting data sets or the students' own data. Topics: data, visualization, web. Prerequisites: some experience with general programming is required to understand the lectures and assignments.
Terms: Aut

Units: 1

Grading: Satisfactory/No Credit
Instructors:
Deriso, D. (PI)
CME 192: Introduction to MATLAB
This short course runs for the first eight weeks of the quarter and is offered each quarter during the academic year. It is highly recommended for students with no prior programming experience who are expected to use MATLAB in math, science, or engineering courses. It will consist of interactive lectures and applicationbased assignments.nThe goal of the short course is to make students fluent in MATLAB and to provide familiarity with its wide array of features. The course covers an introduction of basic programming concepts, data structures, and control/flow; and an introduction to scientific computing in MATLAB, scripts, functions, visualization, simulation, efficient algorithm implementation, toolboxes, and more.
Terms: Aut

Units: 1

Grading: Satisfactory/No Credit
Instructors:
Craig, A. (PI)
CME 193: Introduction to Scientific Python
This short course runs for the first four weeks of the quarter. It is recommended for students who are familiar with programming at least at the level of CS106A and want to translate their programming knowledge to Python with the goal of becoming proficient in the scientific computing and data science stack. Lectures will be interactive with a focus on real world applications of scientific computing. Technologies covered include Numpy, SciPy, Pandas, Scikitlearn, and others. Topics will be chosen from Linear Algebra, Optimization, Machine Learning, and Data Science. Prior knowledge of programming will be assumed, and some familiarity with Python is helpful, but not mandatory.
Terms: Aut, Win, Spr

Units: 1

Grading: Satisfactory/No Credit
CME 195: Introduction to R (STATS 195)
This short course runs for the first four weeks of the quarter and is offered in fall and spring. It is recommended for students who want to use R in statistics, science, or engineering courses and for students who want to learn the basics of R programming. The goal of the short course is to familiarize students with R's tools for scientific computing. Lectures will be interactive with a focus on learning by example, and assignments will be applicationdriven. No prior programming experience is needed. Topics covered include basic data structures, File I/O, graphs, control structures, etc, and some useful packages in R.
Terms: Aut, Spr

Units: 1

Grading: Satisfactory/No Credit
Instructors:
Michael, H. (PI)
;
Nguyen, L. (PI)
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