## CME 99: WiDS Datathon Independent Study (DATASCI 99)

This independent study offers students the opportunity to participate in the WiDS Datathon for 1-unit of credit. The WiDS Datathon is an annual and global event that encourages data scientists of all levels to discover and hone their data science skills while solving an interesting and critical social impact challenge. The 2023 Challenge, "Data Science for Subseasonal Forecast", centers on climate change and is in partnership with Climate Change AI (CCAI). Accurate long-term forecasts of temperature and precipitation is crucial for mitigating the effects of climate change (i.e. preparing for droughts and other wet weather extremes). Such forecasts can potentially impact many industries (e.g. agriculture, energy, disaster planning) in countries across the globe. Currently, purely physics-based models dominate short-term weather forecasting. But these models have a limited forecast horizon. The availability of meteorological data offers an opportunity for data scientists to improve subse
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This independent study offers students the opportunity to participate in the WiDS Datathon for 1-unit of credit. The WiDS Datathon is an annual and global event that encourages data scientists of all levels to discover and hone their data science skills while solving an interesting and critical social impact challenge. The 2023 Challenge, "Data Science for Subseasonal Forecast", centers on climate change and is in partnership with Climate Change AI (CCAI). Accurate long-term forecasts of temperature and precipitation is crucial for mitigating the effects of climate change (i.e. preparing for droughts and other wet weather extremes). Such forecasts can potentially impact many industries (e.g. agriculture, energy, disaster planning) in countries across the globe. Currently, purely physics-based models dominate short-term weather forecasting. But these models have a limited forecast horizon. The availability of meteorological data offers an opportunity for data scientists to improve subseasonal forecasts by blending physics-based forecasts with machine learning. To learn more, visit:
https://www.widsconference.org/datathon.htmlStudents may participate in this independent study in teams of 1-4. To qualify for official participation in the datathon, at least half of each team must identify as women. To receive credit, the team will participate in the Datathon and write a report detailing their submission and reflecting on their experience. Interested students should register for the course, and sign up as a team using this form:
https://forms.gle/LyX3yNU7dLnTCux1A. To find other students interested in forming a team, go here:
https://docs.google.com/presentation/d/1UvutEFtYFeCkLkwnpU01R5V5WmJeMi4kVkaZYHxSiAY/edit?usp=sharing

Terms: Win
| Units: 1
| Repeatable
4 times
(up to 4 units total)

## 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, Sum
| Units: 5
| UG Reqs: GER:DB-Math, WAY-FR

## CME 102ACE: 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 should submit application for enrollment at:
https://engineering.stanford.edu/students/programs/engineering-diversity-programs/additional-calculus-engineers before study list deadline. It is recommended students enroll in the regular section (
CME102) prior to submitting application.

Terms: Aut, Win
| Units: 6
| 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-FR, WAY-AQR

Instructors:
Khayms, V. (PI)
;
Kopff, A. (TA)
;
Lan, H. (TA)
;
Mikou, M. (TA)
;
Nagafuchi, Y. (TA)
;
Xu, B. (TA)

## CME 106ACE: Introduction to Probability and Statistics for Engineers, ACE

Students attend
CME106/ENGR155C lectures with additional recitation sessions; two to four hours per week, emphasizing engineering mathematical applications and collaboration methods. Prerequisite: students should submit application for enrollment at:
https://engineering.stanford.edu/students/programs/engineering-diversity-programs/additional-calculus-engineers before study list deadline. It is recommended students enroll in the regular section (
CME106) prior to submitting application.

Terms: Win
| Units: 6

Instructors:
Khayms, V. (PI)

## CME 108: Introduction to Scientific Computing

Introduction to Scientific Computing Numerical computation for mathematical, computational, physical sciences and engineering: error analysis, floating-point arithmetic, nonlinear equations, numerical solution of systems of algebraic equations, banded matrices, least squares, unconstrained optimization, polynomial interpolation, numerical differentiation and integration, numerical solution of ordinary differential equations, truncation error, numerical stability for time dependent problems and stiffness. Implementation of numerical methods in MATLAB programming assignments. Prerequisites:
CME 100, 102 or
MATH 51, 52, 53; prior programming experience (MATLAB or other language at level of
CS 106A or higher).

Terms: Win, Sum
| Units: 3
| UG Reqs: WAY-AQR, WAY-FR, GER:DB-EngrAppSci

## CME 192: Introduction to MATLAB

This short course runs for the first four weeks/eight lectures 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 application-based assignments. The 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, Win, Spr
| Units: 1

Instructors:
Saad, N. (PI)

## CME 193: Introduction to Scientific Python

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, Scikit-learn, 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

Instructors:
Zu, B. (PI)

## CME 204: Partial Differential Equations in Engineering (ME 300B)

Geometric interpretation of partial differential equation (PDE) characteristics; solution of first order PDEs and classification of second-order PDEs; self-similarity; separation of variables as applied to parabolic, hyperbolic, and elliptic PDEs; special functions; eigenfunction expansions; the method of characteristics. If time permits, Fourier integrals and transforms, Laplace transforms. Prerequisite:
CME 200/
ME 300A, equivalent, or consent of instructor.

Terms: Win
| Units: 3

Instructors:
Shaqfeh, E. (PI)

## CME 216: Machine Learning for Computational Engineering. (ME 343)

Linear and kernel support vector machines, deep learning, deep neural networks, generative adversarial networks, physics-based machine learning, forward and reverse mode automatic differentiation, optimization algorithms for machine learning, TensorFlow, PyTorch.

Terms: Win
| Units: 3

Instructors:
Darve, E. (PI)

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