Courses tagged with "Information environments" (1105)

This course concerns the theory and practice of optical methods in engineering and system design, with an emphasis on diffraction, statistical optics, holography, and imaging. It provides the engineering methodology skills necessary to incorporate optical components in systems serving diverse areas such as precision engineering and metrology, bio-imaging, and computing (sensors, data storage, communication in multi-processor systems). Experimental demonstrations and a design project are included.

6.637 covers the fundamentals of optical signals and modern optical devices and systems from a practical point of view. Its goal is to help students develop a thorough understanding of the underlying physical principles such that device and system design and performance can be predicted, analyzed, and understood.

Most optical systems involve the use of one or more of the following: sources (e.g., lasers and light-emitting diodes), light modulation components (e.g., liquid-crystal light modulators), transmission media (e.g., free space or fibers), photodetectors (e.g., photodiodes, photomultiplier tubes), information storage devices (e.g., optical disk), processing systems (e.g., imaging and spatial filtering systems) and displays (LCOS microdisplays). These are the topics covered by this course.

This course introduces the principal algorithms for linear, network, discrete, nonlinear, dynamic optimization and optimal control. Emphasis is on methodology and the underlying mathematical structures. Topics include the simplex method, network flow methods, branch and bound and cutting plane methods for discrete optimization, optimality conditions for nonlinear optimization, interior point methods for convex optimization, Newton's method, heuristic methods, and dynamic programming and optimal control methods.

This course focuses on developing oral presentation skills through practice, self-evaluation, and in-class feedback. Topics include slide preparation, answering difficult questions, explaining technical details and presenting to a general audience.

This course covers modern and advanced methods of elucidation of the structures of organic molecules, including NMR, MS, and IR (among others). The fundamental physical and chemical principles of each method will be discussed. The major emphasis of this course is on structure determination by way of interpreting the data (generally in the form of a spectrum or spectra) that each method provides.

This course explores organizational concepts and research methods that explain the performance and development of military organizations in peace and war. Classic studies are reviewed. Approaches to current policy problems based on theoretical insights into military organizations and practices are also considered. The class stresses development of new theory.

This reading course seeks to provide students with frameworks for understanding organizational behavior and research tools for studying them. It offers an overview of major theories and approaches, and an opportunity to discuss major and classic works on military and non-military organizations. For advanced graduate students, preferably those selecting a dissertation topic.

This course in organizational economics prepares doctoral students for further study in the field. The course introduces the classic papers and some recent research. The material is organized into the following modules: boundaries of the firm, employment in organizations, decision-making in organizations, and structures and processes in organizations. Each class session covers a few leading papers.

This course was joint-taught between faculty of the Massachusetts Institute of Technology and Harvard University. The Harvard course is Economics 2670 Organizational Economics.

15.317 Organizational Leadership and Change focuses on practical experience that blends theory and practice. Students reflect on prior leadership experiences and then apply lessons learned to further develop their leadership capabilities. The course requires active participation in all leadership classes and/or activities as well as short deliverables throughout the program.

Organizational Processes enhances students' ability to take effective action in complex organizational settings by providing the analytic tools needed to analyze, manage, and lead the organizations of the future. Emphasis is placed on the importance of the organizational context in influencing which individual styles and skills are effective. The subject centers on three complementary perspectives, or "lenses", on an organization: political, cultural, and strategic design. Students enrolled in this class are also jointly enrolled in 15.328, Team Project, in order to complete a field study of an organizational change initiative. Organizational Processes also operates in conjunction with 15.280, Communication for Managers, by sharing certain assignments and holding some joint classes.

The goal of this doctoral course is to familiarize students with major conceptual frameworks, debates, and developments in contemporary organization theory. This is an inter-disciplinary domain of inquiry drawing primarily from sociology, and secondarily from economics, psychology, anthropology, and political science. The course focuses on inter-organizational processes, and also addresses the economic, institutional and cultural contexts that organizations must face.

This is an introduction to a vast and multifaceted domain of inquiry. Due to time limitations, this course will touch lightly on many important topics, and neglect others entirely; its design resembles more a map than an encyclopedia. Also, given the focus on theoretical matters, methodological issues will move to the background. Empirical material will be used to illustrate how knowledge is produced from a particular standpoint and trying to answer particular questions, leaving the bulk of the discussion on quantitative and qualitative procedures to seminars such as 15.347, 15.348, and the like.

This course introduces new product development. Topics include technology transfer, relations between science and technology, and the innovation process.

This course examines important transformations of organotransition-metal species with an emphasis on basic mechanisms, structure-reactivity relationships, and applications in organic synthesis.

This class examines tools, data, and ideas related to past climate changes as seen in marine, ice core, and continental records. The most recent climate changes (mainly the past 500,000 years, ranging up to about 2 million years ago) will be emphasized. Quantitative tools for the examination of paleoceanographic data will be introduced (statistics, factor analysis, time series analysis, simple climatology).

This course introduces fundamentals of shared and distributed memory programming, teaches you how to code using openMP and MPI respectively, and provides hands-on experience of parallel computing geared towards numerical applications.

8.811, Particle Physics II, describes essential research in High Energy Physics. We derive the Standard Model (SM) first using a bottom up method based on Unitarity, in addition to the usual top down method using SU3xSU2xU1. We describe and analyze several classical experiments, which established the SM, as examples on how to design experiments. Further topics include heavy flavor physics, high-precision tests of the Standard Model, neutrino oscillations, searches for new phenomena (compositeness, supersymmetry, technical color, and GUTs), and discussion of expectations from future accelerators (B factory, LHC, large electron-positron linear colliders, etc). The term paper requires the students to have constant discussions with the instructor throughout the semester on theories, physics, measurables, signatures, detectors, resolution, background identification and elimination, signal to noise and statistical analysis.

This course covers the basics of general relativity, standard big bang cosmology, thermodynamics of the early universe, cosmic background radiation, primordial nucleosynthesis, basics of the standard model of particle physics, electroweak and QCD phase transition, basics of group theory, grand unified theories, baryon asymmetry, monopoles, cosmic strings, domain walls, axions, inflationary universe, and structure formation.

This class deals with the fundamentals of characterizing and recognizing patterns and features of interest in numerical data. We discuss the 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. We also cover 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 are also talked about in the class.

The applications of pattern recognition techniques to problems of machine vision is the main focus for this course. Topics covered include, an overview of problems of machine vision and pattern classification, image formation and processing, feature extraction from images, biological object recognition, bayesian decision theory, and clustering.

This seminar-format course provides an in-depth presentation and discussion of how engineering and biological approaches can be combined to solve problems in science and technology, emphasizing integration of biological information and methodologies with engineering analysis, synthesis, and design. Emphasis is placed on molecular mechanisms underlying cellular processes, including signal transduction, gene expression networks, and functional responses.