Online courses directory (6)

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Starts : 2006-09-01
16 votes
MIT OpenCourseWare (OCW) Free Engineering Biological Engineering MIT OpenCourseWare Undergraduate

This course covers sensing and measurement for quantitative molecular/cell/tissue analysis, in terms of genetic, biochemical, and biophysical properties. Methods include light and fluorescence microscopies; electro-mechanical probes such as atomic force microscopy, laser and magnetic traps, and MEMS devices; and the application of statistics, probability and noise analysis to experimental data. Enrollment preference is given to juniors and seniors.

Starts : 2006-09-01
14 votes
MIT OpenCourseWare (OCW) Free Engineering Biological Engineering MIT OpenCourseWare Undergraduate

This course covers the analytical, graphical, and numerical methods supporting the analysis and design of integrated biological systems. Topics include modularity and abstraction in biological systems, mathematical encoding of detailed physical problems, numerical methods for solving the dynamics of continuous and discrete chemical systems, statistics and probability in dynamic systems, applied local and global optimization, simple feedback and control analysis, statistics and probability in pattern recognition.

An official course Web site and Wiki is maintained on OpenWetWare: 20.181 Computation for Biological Engineers.

Starts : 2009-09-01
12 votes
MIT OpenCourseWare (OCW) Free Engineering Mechanical Engineering MIT OpenCourseWare Undergraduate

This course covers the design, construction, and testing of field robotic systems, through team projects with each student responsible for a specific subsystem. Projects focus on electronics, instrumentation, and machine elements. Design for operation in uncertain conditions is a focus point, with ocean waves and marine structures as a central theme. Topics include basic statistics, linear systems, Fourier transforms, random processes, spectra, ethics in engineering practice, and extreme events with applications in design.

Starts : 2005-02-01
14 votes
MIT OpenCourseWare (OCW) Free Engineering Graduate Mechanical Engineering MIT OpenCourseWare

The course covers the basic techniques for evaluating the maximum forces and loads over the life of a marine structure or vehicle, so as to be able to design its basic configuration. Loads and motions of small and large structures and their short-term and long-term statistics are studied in detail and many applications are presented in class and studied in homework and laboratory sessions. Issues related to seakeeping of ships are studied in detail. The basic equations and issues of maneuvering are introduced at the end of the course. Three laboratory sessions demonstrate the phenomena studied and provide experience with experimental methods and data processing.

This course was originally offered in Course 13 (Ocean Engineering) as 13.42.

Starts : 2005-02-01
8 votes
MIT OpenCourseWare (OCW) Free Engineering Civil and Environmental Engineering Graduate MIT OpenCourseWare

This class covers quantitative analysis of uncertainty and risk for engineering applications. Fundamentals of probability, random processes, statistics, and decision analysis are covered, along with random variables and vectors, uncertainty propagation, conditional distributions, and second-moment analysis. System reliability is introduced. Other topics covered include Bayesian analysis and risk-based decision, estimation of distribution parameters, hypothesis testing, simple and multiple linear regressions, and Poisson and Markov processes. There is an emphasis placed on real-world applications to engineering problems.

Starts : 2008-09-01
9 votes
MIT OpenCourseWare (OCW) Free Engineering Civil and Environmental Engineering MIT OpenCourseWare Undergraduate

This course gives an introduction to probability and statistics, with emphasis on engineering applications. Course topics include events and their probability, the total probability and Bayes' theorems, discrete and continuous random variables and vectors, uncertainty propagation and conditional analysis. Second-moment representation of uncertainty, random sampling, estimation of distribution parameters (method of moments, maximum likelihood, Bayesian estimation), and simple and multiple linear regression. Concepts illustrated with examples from various areas of engineering and everyday life.

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