printer friendly pageCME 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
CME 200: Linear Algebra with Application to Engineering Computations (ME 300A)
Computer based solution of systems of algebraic equations obtained from engineering problems and eigen-system analysis, Gaussian elimination, effect of round-off error, operation counts, banded matrices arising from discretization of differential equations, ill-conditioned matrices, matrix theory, least square solution of unsolvable systems, solution of non-linear algebraic equations, eigenvalues and eigenvectors, similar matrices, unitary and Hermitian matrices, positive definiteness, Cayley-Hamilton theory and function of a matrix and iterative methods. Prerequisite: familiarity with computer programming, and
MATH51.
Terms: Aut
| Units: 3
Instructors:
Aboumrad, G. (PI)
;
Iaccarino, G. (PI)
;
AKULI, N. (TA)
...
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Instructors:
Aboumrad, G. (PI)
;
Iaccarino, G. (PI)
;
AKULI, N. (TA)
;
Benjamin, M. (TA)
;
Kloker, L. (TA)
;
Madden, I. (TA)
;
Zainana, S. (TA)
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:
Khanwale, M. (PI)
;
Mirjalili, S. (PI)
;
AKULI, N. (TA)
...
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Instructors:
Khanwale, M. (PI)
;
Mirjalili, S. (PI)
;
AKULI, N. (TA)
;
Brown, R. (TA)
;
Goel, P. (TA)
CME 206: Introduction to Numerical Methods for Engineering (ME 300C)
Numerical methods from a user's point of view. Lagrange interpolation, splines. Integration: trapezoid, Romberg, Gauss, adaptive quadrature; numerical solution of ordinary differential equations: explicit and implicit methods, multistep methods, Runge-Kutta and predictor-corrector methods, boundary value problems, eigenvalue problems; systems of differential equations, stiffness. Emphasis is on analysis of numerical methods for accuracy, stability, and convergence. Introduction to numerical solutions of partial differential equations; Von Neumann stability analysis; alternating direction implicit methods and nonlinear equations. Prerequisites:
CME 200/
ME 300A,
CME 204/
ME 300B.
Terms: Spr
| Units: 3
CME 209: Mathematical Modeling of Biological Systems (BIOE 209)
The course covers mathematical and computational techniques needed to solve advanced problems encountered in applied bioengineering. Fundamental concepts are presented in the context of their application to biological and physiological problems including cancer, cardiovascular disease, infectious disease, and systems biology. Topics include Taylor's Series expansions, parameter estimation, regression, nonlinear equations, linear systems, optimization, numerical differentiation and integration, stochastic methods, ordinary differential equations and Fourier series. Python, Matlab and other software will be used for weekly assignments and projects.Prerequisites:
Math 51, 52, 53; prior programming experience (Matlab or other language at level of
CS 106a or higher)
Terms: Aut
| Units: 3
Instructors:
Marsden, A. (PI)
;
Papamanolis, L. (TA)
CME 213: Introduction to parallel computing using MPI, openMP, and CUDA (ME 339)
This class will give hands-on experience with programming multicore processors, graphics processing units (GPU), and parallel computers. The focus will be on the message passing interface (MPI, parallel clusters) and the compute unified device architecture (CUDA, GPU). Topics will include multithreaded programs, GPU computing, computer cluster programming, C++ threads, OpenMP, CUDA, and MPI. Pre-requisites include C++, templates, debugging, UNIX, makefile, numerical algorithms (differential equations, linear algebra).
Terms: Spr
| Units: 3
CME 215: Machine Learning and the Physical Sciences (GEOPHYS 148, GEOPHYS 248)
This course provides a survey of the rapidly growing field of machine learning in the physical sciences. It covers various areas such as inverse problems, emulating physical processes, model discovery given data, and solution discovery given equations. It both introduces the background knowledge required to implement physics-informed deep learning and provides practical in-class coding exercises. Students have the opportunity to apply this emerging methodology to their own research interests across all fields of the physical sciences, including geophysics, climate, fluids, or other systems where the same technique applies. Students develop individual projects throughout the semester. Recommended Prerequisite: Calculus (e.g.
Math 21), Differential Equations (e.g.
MATH 53 or
PHYSICS 111) or equivalents.
Terms: Spr
| Units: 3
Instructors:
Lai, C. (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:
Patel, D. (PI)
;
Woringer, P. (TA)
CME 218: Applied Data Science (MS&E 218)
This is a multidisciplinary graduate level course designed to give students hands-on experience working in teams through real-world project-based research and experiential classroom activities. Students work in dynamic teams with the support of course faculty and mentors, researching preselected topics. Students apply a computational and data analytics lens and use design thinking methodology. The course exposes students to important techniques in applied data science as well as to the soft skills necessary for success in applied data science, such as ethics, unintended consequences and team building. Enrollment by application only. Graduate students only. The course application closes Sept 25, 2023. Application and more information:
https://forms.gle/gzGXkJmGMVYuJabK7
Terms: Aut
| Units: 3
| Repeatable
2 times
(up to 6 units total)
Instructors:
Hanson, K. (PI)
;
Udell, M. (PI)
CME 229: Applications of machine learning to electronic markets
In this 10-week course, students will learn to apply the techniques of modern machine learning (such as neural networks, reinforcement learning, generative adversarial networks, etc.) to electronic markets. Topics covered will include the fundamentals of financial electronic markets, market simulation, reinforcement learning for market-making and algorithmic execution, as well as predictive and generative modeling for financial markets. Assignments for this course will consist of a mixture of theoretical and coding exercises, and will expose students to real-life financial markets datasets. Throughout the course, students will be introduced to the latest academic and industry research papers, and will complete the course by working on a project of their choice. Course prerequisites: familiarity with optimization and statistics, and ability to code in Python. Open to Graduate Students and Senior-status Undergraduates. Others may request instructor permission to enroll.
Terms: Win
| Units: 3
Instructors:
Vyetrenko, S. (PI)
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