Courses tagged with "Infor" (2510)

Parkinson's disease (PD) is a chronic, progressive, degenerative disease of the brain that produces movement disorders and deficits in executive functions, working memory, visuospatial functions, and internal control of attention. It is named after James Parkinson (1755-1824), the English neurologist who described the first case.

This six-week summer workshop explored different aspects of PD, including clinical characteristics, structural neuroimaging, neuropathology, genetics, and cognitive function (mental status, cognitive control processes, working memory, and long-term declarative memory).  The workshop did not take up the topics of motor control, nondeclarative memory, or treatment. 

Humans are social animals; social demands, both cooperative and competitive, structure our development, our brain and our mind. This course covers social development, social behaviour, social cognition and social neuroscience, in both human and non-human social animals. Topics include altruism, empathy, communication, theory of mind, aggression, power, groups, mating, and morality. Methods include evolutionary biology, neuroscience, cognitive science, social psychology and anthropology.

This course is offered to undergraduates and introduces students to the formulation, methodology, and techniques for numerical solution of engineering problems. Topics covered include: fundamental principles of digital computing and the implications for algorithm accuracy and stability, error propagation and stability, the solution of systems of linear equations, including direct and iterative techniques, roots of equations and systems of equations, numerical interpolation, differentiation and integration, fundamentals of finite-difference solutions to ordinary differential equations, and error and convergence analysis. The subject is taught the first half of the term.

This subject was originally offered in Course 13 (Department of Ocean Engineering) as 13.002J. In 2005, ocean engineering became part of Course 2 (Department of Mechanical Engineering), and this subject was renumbered 2.993J.

This subject provides an introduction to modeling and simulation, covering continuum methods, atomistic and molecular simulation, and quantum mechanics. Hands-on training is provided in the fundamentals and applications of these methods to key engineering problems. The lectures provide exposure to areas of application based on the scientific exploitation of the power of computation. We use web based applets for simulations, thus extensive programming skills are not required.

This course applies the concepts of reaction rate, stoichiometry and equilibrium to the analysis of chemical and biological reacting systems, derivation of rate expressions from reaction mechanisms and equilibrium or steady state assumptions, design of chemical and biochemical reactors via synthesis of chemical kinetics, transport phenomena, and mass and energy balances. Topics covered include: chemical/biochemical pathways; enzymatic, pathway, and cell growth kinetics; batch, plug flow and well-stirred reactors for chemical reactions and cultivations of microorganisms and mammalian cells; heterogeneous and enzymatic catalysis; heat and mass transport in reactors, including diffusion to and within catalyst particles and cells or immobilized enzymes.

This course covers topics in time-dependent quantum mechanics, spectroscopy, and relaxation, with an emphasis on descriptions applicable to condensed phase problems and a statistical description of ensembles.

6.453 Quantum Optical Communication is one of a collection of MIT classes that deals with aspects of an emerging field known as quantum information science. This course covers Quantum Optics, Single-Mode and Two-Mode Quantum Systems, Multi-Mode Quantum Systems, Nonlinear Optics, and Quantum System Theory.

Explore the future through modeling, reading, and discussion in an open-ended seminar! Our fields of interest will include changes in science and technology, culture and lifestyles, and dominant paradigms and societies.

This course presents the fundamentals of object-oriented software design and development, computational methods and sensing for engineering, and scientific and managerial applications. It cover topics, including design of classes, inheritance, graphical user interfaces, numerical methods, streams, threads, sensors, and data structures. Students use Java^® programming language to complete weekly software assignments.

How is 1.00 different from other intro programming courses offered at MIT?

1.00 is a first course in programming. It assumes no prior experience, and it focuses on the use of computation to solve problems in engineering, science and management. The audience for 1.00 is non-computer science majors. 1.00 does not focus on writing compilers or parsers or computing tools where the computer is the system; it focuses on engineering problems where the computer is part of the system, or is used to model a physical or logical system.

1.00 teaches the Java programming language, and it focuses on the design and development of object-oriented software for technical problems. 1.00 is taught in an active learning style. Lecture segments alternating with laboratory exercises are used in every class to allow students to put concepts into practice immediately; this teaching style generates questions and feedback, and allows the teaching staff and students to interact when concepts are first introduced to ensure that core ideas are understood. Like many MIT classes, 1.00 has weekly assignments, which are programs based on actual engineering, science or management applications. The weekly assignments build on the class material from the previous week, and require students to put the concepts taught in the small in-class labs into a larger program that uses multiple elements of Java together.

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.

This subject provides an introduction to the mechanics of materials and structures. You will be introduced to and become familiar with all relevant physical properties and fundamental laws governing the behavior of materials and structures and you will learn how to solve a variety of problems of interest to civil and environmental engineers. While there will be a chance for you to put your mathematical skills obtained in 18.01, 18.02, and eventually 18.03 to use in this subject, the emphasis is on the physical understanding of why a material or structure behaves the way it does in the engineering design of materials and structures.

This course examines the policy, politics, planning, and engineering of transportation systems in urban areas, with a special focus on the Boston area. It covers the role of the federal, state, and local government and the MPO, public transit in the era of the automobile, analysis of current trends and pattern breaks; analytical tools for transportation planning, traffic engineering, and policy analysis; the contribution of transportation to air pollution, social costs, and climate change; land use and transportation interactions, and more. Transportation sustainability is a central theme throughout the course, as well as consideration of if and how it is possible to resolve the tension between the three E's (environment, economy, and equity). The goal of this course is to elicit discussion, stimulate independent thinking, and encourage students to understand and challenge the "conventional wisdom" of transportation planning.

We will explore the mathematical strategies behind popular games, toys, and puzzles. Topics covered will combine basic fundamentals of game theory, probability, group theory, and elementary programming concepts. Each week will consist of a lecture and discussion followed by game play to implement the concepts learned in class.

This is a variation on 18.02 Multivariable Calculus. It covers the same topics as in 18.02, but with more focus on mathematical concepts.

Acknowledgement

Prof. McKernan would like to acknowledge the contributions of Lars Hesselholt to the development of this course.

This course provides an introduction to the study of environmental phenomena that exhibit both organized structure and wide variability—i.e., complexity. Through focused study of a variety of physical, biological, and chemical problems in conjunction with theoretical models, we learn a series of lessons with wide applicability to understanding the structure and organization of the natural world. Students also learn how to construct minimal mathematical, physical, and computational models that provide informative answers to precise questions.

This course is appropriate for advanced undergraduates. Beginning graduate students are encouraged to register for 12.586 (graduate version of 12.086). Students taking the graduate version complete different assignments.

A great variety of processes affect the surface of the Earth. Topics to be covered are production and movement of surficial materials; soils and soil erosion; precipitation; streams and lakes; groundwater flow; glaciers and their deposits. The course combines aspects of geology, climatology, hydrology, and soil science to present a coherent introduction to the surface of the Earth, with emphasis on both fundamental concepts and practical applications, as a basis for understanding and intelligent management of the Earth's physical and chemical environment.

This course provides an exciting, eye-opening, and thoroughly useful inquiry into what it takes to live an extraordinary life, on your own terms. The instructors address what it takes to succeed, to be proud of your life, and to be happy in it. Participants tackle career satisfaction, money, body, vices, and relationship to themselves. They learn how to confront issues in their lives, how to live life, and how to learn from it.

A short version of this course meets during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month. Then this semester-long extension of the IAP course is taught to interested members of the MIT community. This not-for-credit course is sponsored by the Department of Science, Technology, and Society. A similar, semester-long version of this course is taught in the Sloan Fellows Program.

Acknowledgment

The instructors would like to thank Prof. David Mindell for his sponsorship of this course, his hopes for its continued expansion, and his commitment to the well-being of MIT students.

6.004 offers an introduction to the engineering of digital systems. Starting with MOS transistors, the course develops a series of building blocks — logic gates, combinational and sequential circuits, finite-state machines, computers and finally complete systems. Both hardware and software mechanisms are explored through a series of design examples.

6.004 is required material for any EECS undergraduate who wants to understand (and ultimately design) digital systems. A good grasp of the material is essential for later courses in digital design, computer architecture and systems. The problem sets and lab exercises are intended to give students "hands-on" experience in designing digital systems; each student completes a gate-level design for a reduced instruction set computer (RISC) processor during the semester.

Differential Equations are the language in which the laws of nature are expressed. Understanding properties of solutions of differential equations is fundamental to much of contemporary science and engineering. Ordinary differential equations (ODE's) deal with functions of one variable, which can often be thought of as time.

This course introduces three main types of partial differential equations: diffusion, elliptic, and hyperbolic. It includes mathematical tools, real-world examples and applications.