Other EGR Courses
Additional EGR courses are those with focused computer science, engineering or mathematical content. These courses are relevant to students beyond the home department. Currently the following courses are in this category:
COS 126 General Computer Science (also EGR 126) Fall, Spring QR
An introduction to computer science in the context of scientific, engineering, and commercial applications. The goal of the course is to teach basic principles and practical issues, while at the same time preparing students to use computers effectively for applications in computer science, physics, biology, chemistry, engineering, and other disciplines. Topics include: hardware and software systems; programming in Java; algorithms and data structures; fundamental principles of computation; and scientific computing, including simulation, optimization, and data analysis. Two lectures, two classes. Professor: D. Clark
EGR 191 An Integrated Introduction to Engineering, Mathematics, Physics (also MAT 191/PHY 191) Fall ST
Taken concurrently with EGR/MAT/PHY 192. An integrated course that covers the material of PHY 103 (General Physics: Mechanics and Thermodynamics) and MAT 201 (Multivariable Calculus) with the emphasis on applications to engineering. The physics part of the course discusses mechanics with applications to fluid mechanics, wave phenomena, and thermodynamics. Concurrently, the necessary mathematical background and tools will be taught, including vector calculus, partial derivatives and matrices, line integrals, simple differential equations, surface and volume integrals, and Green's, Stokes', and divergence theorems. Professors: P. Meyers, I. Daubechies, P. Debenedetti
EGR 192 An Integrated Introduction to Engineering, Mathematics, Physics (also MAT 192/PHY 192) Fall QR
Taken concurrently with EGR/MAT/PHY 191. An integrated course that covers the material of PHY 103 (General Physics: Mechanics and Thermodynamics) and MAT 201 (Multivariable Calculus) with the emphasis on applications to engineering. The physics part of the course discusses mechanics with applications to fluid mechanics, wave phenomena, and thermodynamics. Concurrently, the necessary mathematical background and tools will be taught, including vector calculus, partial derivatives and matrices, line integrals, simple differential equations, surface and volume integrals, and Green's, Stokes', and divergence theorems. Professors: P.Meyers, I. Daubechies, P. Debenedetti
EGR 193 An Integrated Introduction to Engineering, Mathematics, Physics (also MAT 193/PHY 193) Spring ST
Taken concurrently with EGR/MAT/PHY 194. These two courses will address the material of PHY 104 and offer an introduction to the various disciplines of engineering. The physics part of the course covers the basic laws of electricity, magnetism, and optics, from Coulomb's law to Maxwell's equations and the prediction of electromagnetic waves. The course concludes with an introduction of quantum theory with a treatment of matter waves, quantization, and the Schroedinger equation. Students who were enrolled in both EGR/MAT/PHY 191 and 192 concurrently in the fall semester will continue in the spring in both EGR/MAT/PHY 193 and 194. Professors: P.Debenedetti, F. Calaprice
EGR 194 An Integrated Introduction to Engineering, Mathematics and Physics (also MAT 194/PHY 194) Spring ST
Taken concurrently with EGR/MAT/PHY 193. These two courses will address the material of PHY 104 and offer an introduction to the various disciplines of engineering. The engineering part of the course is a project-based sequence (Energy Conversion and the Environment, Robotic Remote Sensing, and Wireless Image & Video Transmission) that covers engineering disciplines and their relationship to the principles of physics and mathematics. Students who were enrolled in both EGR/MAT/PHY 191 and 192 concurrently in the fall semester will continue in the spring in both EGR/MAT/PHY 193 and 194. Professors: P. Debenedetti, M. Littman, S. Lyon
ORF 245 Fundamentals of Engineering Statistics (also EGR 245) Fall, Spring QR
Statistics is the science of turning data into information. In this course, we will study the basic methods by which statisticians attempt to model real world phenomena and extract information from data. These will include many of the standard tools of statistical inference, their mathematical foundations as well as exploratory and graphical data analysis techniques. Professor: Staff
MAE 305 Mathematics in Engineering I (also MAT 205/EGR 305) Fall, Spring QR
An introduction to ordinary differential equations. Use of numerical methods. Equations of a single variable and systems of linear equations. Method of undermined coefficients and method of variation of parameters. Series solutions. Use of eigenvalues and eigenvectors. Laplace transforms. Nonlinear equations and stability; phase portraits. Partial differential equations via separation of variables. Sturm-Liouville theory. Three lectures. Prerequisites: MAT 201 or 203, 202 or 204. Professors: M. Kostin, L. Martinelli
ORF 307 Optimization (also EGR 307) Spring
Optimization of deterministic systems, focusing on linear programming. Model formulations, the simplex method, sensitivity analysis, duality theory, network models, nonlinear programming. Applications to a variety of problems in optimal allocation of resources, transportation systems, and finance. Professor: A. d'Aspremont
ORF 309 Probability and Stochastic Systems (also MAT 309 /EGR 309) Fall
An introduction to probability and its applications. Random variables, expectation, independence. Poisson processes, Markov chains, and Brownian motion. Stochastic models of queues, population dynamics, and reliability. Professor: E.Cinlar