The Stanford Oil Wealth Management dataset is the result of a collaborative. February 2008 workshop of Stanford’s Program on Energy and Sustainable Development.Institute for Computational and Mathematical Engineering. Courses. CME 2. 0Q. 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. 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. Examples and applications drawn from various engineering fields. Prerequisites: 1. Stanford Investment Group provides personalized wealth management through financial advisors to help you build wealth and reach your long-term financial goals. The Freeman Spogli Institute (FSI) is Stanford University's primary forum for interdisciplinary research on key international issues and challenges. Tolleson Wealth Management Hosts. Entrepreneur Magazine voted his interview at Stanford. This event is a part of The Tolleson Wealth Management Family Education Program. The Tolleson Wealth Management Family Education. Browse hundreds of courses and specializations in Business, Computer Science, Arts, Humanities, and more. 1000+ courses from schools like Stanford and Yale - no application required. Build career skills in data science. AP credit (Calc BC with 4 or 5, or Calc AB with 5), or MATH 4. Same as: ENGR 1. 54. CME 1. 00. A. Enrollment by department permission only. Prerequisite: must be enrolled in the regular CME1. Application at: https: //engineering. Introduction to MATLAB programming as a basic tool kit for computations. Problems from various engineering fields. Prerequisite: 1. 0 units of AP credit (Calc BC with 4 or 5, or Calc AB with 5), or MATH 4. Recommended: CME1. Same as: ENGR 1. 55. ACME 1. 02. A. Prerequisite: students must be enrolled in the regular section (CME1. Vectors, norm, and angle; linear independence and orthonormal sets; applications to document analysis.
Clustering and the k- means algorithm. Matrices, left and right inverses, QR factorization. Least- squares and model fitting, regularization and cross- validation. Constrained and nonlinear least- squares. Applications include time- series prediction, tomography, optimal control, and portfolio optimization. Prerequisites: MATH 5. CME 1. 00, and basic knowledge of computing (CS 1. A is more than enough, and can be taken concurrently). EE1. 03/CME1. 03 and MATH 1. The focus of EE1. MATH 1. 04 is on algorithms and concepts. Same as: EE 1. 03. CME 1. 04. Fourier series with applications, partial differential equations arising in science and engineering, analytical solutions of partial differential equations. Numerical methods for solution of partial differential equations: iterative techniques, stability and convergence, time advancement, implicit methods, von Neumann stability analysis. Examples and applications from various engineering fields. Prerequisite: CME 1. ENGR 1. 55. A. Same as: ENGR 1. BCME 1. 04. A. Prerequisite: students must be enrolled in the regular section (CME1. Topics in mathematical statistics: random sampling, point estimation, confidence intervals, hypothesis testing, non- parametric tests, regression and correlation analyses; applications in engineering, industrial manufacturing, medicine, biology, and other fields. Prerequisite: CME 1. ENGR1. 54 or MATH 5. Same as: ENGR 1. 55. CCME 1. 08. Implementation of numerical methods in MATLAB programming assignments. Prerequisites: MATH 5. MATLAB or other language at level of CS 1. A or higher). Graduate students should take it for 3 units and undergraduate students should take it for 4 units. Same as: MATH 1. 14. CME 1. 51. 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 industry- standard tools, for example d. 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. Topics: data, visualization and web; will explore different sets of free industry- standard tools, for example d. Advanced topics including immersive 3. D visualization using Google Cardboard and dynamic visualization using sensors are explored. Assignments are interactive online tutorials that focus on visual experimentation with interesting data sets or the students' own data. Prerequisites: intermediate level programming experience is required to understand the lectures and assignments. Students will have the opportunity to pursue open- ended projects in a variety of areas: economics, physics, political science, operations research, etc. Projects can cover (but are not limited to!) topics such as mathematical modeling of real- world phenomena (population dynamics), data- driven applications (movie recommendations) or complex systems in engineering (optimal control). Each team will be paired with a graduate student mentor working in applied and computational mathematics. Prerequisites: CME 1. Recommended: CME 1. CME 1. 92/1. 93. 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. It is recommended for students who are familiar with programming at least at the level of CS1. A 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, Sci. Py, 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. Recommended for students interested in writing parallel programs. Focus is on distributed memory programming via the Message Passing Interface (MPI). Topics include: parallel decomposition, basic communication primitives, collective operations, and debugging. Interactive lectures and homework assignments require writing software. Students should be comfortable and interested in writing software in C/C++ but no prior parallel programming experience is required. 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 application- driven. 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. Same as: STATS 1. CME 1. 96. Topics covered: basic language elements; good programming practices; testing and debugging; verification and validation; differences between Fortran- 7. Fortran- 9. 0 (9. LAPACK; calling Fortran routines from C or C++; performance considerations. The course will be centered around solving . Programming proficiency in C/C++, or other modern compiled language, is required. Familiarity with the GNU development tools (compilers, debuggers, makefiles, etc.) is assumed. Prerequisites: CME 2. Prerequisite: familiarity with computer programming, and MATH5. Same as: ME 3. 00. ACME 2. 04. If time permits, Fourier integrals and transforms, Laplace transforms. Prerequisite: CME 2. ME 3. 00. A, equivalent, or consent of instructor. Same as: ME 3. 00. BCME 2. 06. 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 2. ME 3. 00. A, CME 2. ME 3. 00. B. Same as: ME 3. CCME 2. 07. Advanced version of CME2. CME2. 06 material, includes nonlinear PDEs, multidimensional interpolation and integration and an extended discussion of stability for initial boundary value problems. Recommended for students who have some prior numerical analysis experience. Topics include: 1. D and multi- D interpolation, numerical integration in 1. D and multi- D including adaptive quadrature, numerical solutions of ordinary differential equations (ODEs) including stability, numerical solutions of 1. D and multi- D linear and nonlinear partial differential equations (PDEs) including concepts of stability and accuracy. Prerequisites: linear algebra, introductory numerical analysis (CME 1. Same as: AA 2. 14. A, GEOPHYS 2. 17. CME 2. 11. Software design principles including time and space complexity analysis, data structures, object- oriented design, decomposition, encapsulation, and modularity are emphasized. Usage of campus wide Linux compute resources: login, file system navigation, editing files, compiling and linking, file transfer, etc. Versioning and revision control, software build utilities, and the La.
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