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Course Criteria
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3.00 Credits
Fundamentals of characterizing and recognizing patterns and features of interest in numerical data. Basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. Decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research. Knowledge of probability theory and linear algebra required. Limited to 20.
Prerequisite:
Prereq: Permission of instructor
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3.00 Credits
Topics in surface modeling: b-splines, non-uniform rational b-splines, physically based deformable surfaces, sweeps and generalized cylinders, offsets, blending and filleting surfaces. Non-linear solvers and intersection problems. Solid modeling: constructive solid geometry, boundary representation, non-manifold and mixed-dimension boundary representation models, octrees. Robustness of geometric computations. Interval methods. Finite and boundary element discretization methods for continuum mechanics problems. Scientific visualization. Variational geometry. Tolerances. Inspection methods. Feature representation and recognition. Shape interrogation for design, analysis, and manufacturing. Involves analytical and programming assignments.
Prerequisite:
Prereq: Permission of instructor
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3.00 Credits
Core requirement for the M.Eng. program designed to teach students about the roles of today's professional engineer and expose them to team-building skills through lectures, team workshops, and seminars. Topics include: written and oral communication, job placement skills, trends in the engineering and construction industry, risk analysis and risk management, managing public information, proposal preparation, project evaluation, project management, liability, professional ethics, and negotiation. Draws on relevant large-scale projects to illustrate each component of the subject. Grading is based on both individual and team exercises involving written and oral presentations. Limited to Course 1 MEng students.
Prerequisite:
Prereq: -
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3.00 Credits
Theoretical concepts and analysis of wave problems in science and engineering with examples chosen from elasticity, acoustics, geophysics, hydrodynamics, blood flow, nondestructive evaluation, and other applications. Progressive waves, group velocity and dispersion, energy density and transport. Reflection, refraction and transmission of plane waves by an interface. Mode conversion in elastic waves. Rayleigh waves. Waves due to a moving load. Scattering by a two-dimensional obstacle. Reciprocity theorems. Parabolic approximation. Waves on the sea surface. Capillary-gravity waves. Wave resistance. Radiation of surface waves. Internal waves in stratified fluids. Waves in rotating media. Waves in random media.
Prerequisite:
Prereq: 2.003, 18.075
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1.00 Credits
Presentation of the spreadsheet mechanics for the efficient calculation of discounted cash flows and related metrics of project worth; the use of data tables as means of exploring sensitivity analysis; and of simulation to develop the value of options. Intensive module designed for students who are not familiar with the efficient use of Excel. Presented intensively over first week of term.
Prerequisite:
Prereq: None
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3.00 Credits
Covers theory and methods to identify, value, and implement flexibility in design, also known as ?real options?. Topics include definition of uncertainties, simulation of performance for scenarios, screening models to identify desirable flexibility, decision and lattice analysis, and multidimensional economic evaluation. Students demonstrate proficiency through an extended application to a systems design of their choice. Provides a complement to research or thesis projects. Meets with ESD.710 first half of term.
Prerequisite:
Prereq: 1.145 or permission of instructor
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3.00 Credits
Quantitative analysis of uncertainty and risk for engineering applications. Fundamentals of probability, statistics, and decision analysis. Events and their probability, Total Probability and Bayes' Theorems. Random variables and vectors. Bernoulli Trial Sequence and Poisson Point Process models. Conditional distributions and distribution of functions of random variables and vectors. Probabilistic moments, second-moment uncertainty propagation and best linear unbiased estimation theory for variables and vectors. Introduction to system reliability. Estimation of distribution parameters (method of moments, maximum likelihood, Bayesian estimation) and simple and multiple linear regression. Emphasis on application to engineering problems.
Prerequisite:
Prereq: Permission of instructor
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3.00 Credits
Emphasis on three methodologies pertaining to decision making in the presence of uncertainty: reliability and probabilistic risk assessment (RPRA), decision analysis (DA), and cost-benefit analysis (CBA). Risks of particular interest are those associated with large engineering projects such as the development of new products; the building, maintenance and operation of nuclear reactors and space systems. Presents and interprets some of the frameworks helpful for balancing risks and benefits in the situations that typically involve human safety, potential environmental effects, and large financial and technological uncertainties. Review of elementary probability theory and statistics included.
Prerequisite:
Prereq: Calculus II (GIR)
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3.00 Credits
Problem-motivated introduction to methods, models and tools for the analysis and design of transportation networks including their planning, operations and control. Capacity of critical elements of transportation networks. Traffic flows and deterministic and probabilistic delay models. Formulation of optimization models for planning and scheduling of freight, transit and airline systems, and their solution using software packages. User- and system-optimal traffic assignment. Control of traffic flows on highways, urban grids, and airspace.
Prerequisite:
Prereq: 1.010, permission of instructor
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3.00 Credits
Introduces transportation systems analysis, stressing demand and economic aspects. Covers the key principles governing transportation planning, investment, operations, and maintenance. Introduces the microeconomic concepts central to transportation systems. Topics include economic theories of the firm, consumer, and market, demand models, discrete choice analysis, cost models and production functions, and pricing theory. Applications to transportation systems include congestion pricing, technological change, resource allocation, market structure and regulation, revenue forecasting, public and private transportation finance, and project evaluation; covering urban passenger transportation, freight, aviation and intelligent transportation systems.
Prerequisite:
Prereq: Permission of instructor
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