Courses tagged with "Information environments" (1105)

6.541J surveys the structural properties of natural languages, with special emphasis on the sound pattern. Topics covered include: representation of the lexicon; physiology of speech production; articulatory phonetics; acoustical theory of speech production; acoustical and articulatory descriptions of phonetic features and of prosodic aspects of speech; perception of speech; models of lexical access and of speech production and planning; and applications to recognition and generation of speech by machine, and to the study of speech disorders.

This course is for upper-level graduate students who are planning careers in computational neuroscience. This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. It develops basic tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory. It also discusses topics such as boosting and feature selection and examines applications in several areas: Computer Vision, Computer Graphics, Text Classification, and Bioinformatics. The final projects, hands-on applications, and exercises are designed to illustrate the rapidly increasing practical uses of the techniques described throughout the course.

This course discusses the principles and methods of statistical mechanics. Topics covered include classical and quantum statistics, grand ensembles, fluctuations, molecular distribution functions, other concepts in equilibrium statistical mechanics, and topics in thermodynamics and statistical mechanics of irreversible processes.

Statistical Mechanics is a probabilistic approach to equilibrium properties of large numbers of degrees of freedom. In this two-semester course, basic principles are examined. Topics include: Thermodynamics, probability theory, kinetic theory, classical statistical mechanics, interacting systems, quantum statistical mechanics, and identical particles.

This is the second term in a two-semester course on statistical mechanics. Basic principles are examined in this class, such as the laws of thermodynamics and the concepts of temperature, work, heat, and entropy. Topics from modern statistical mechanics are also explored, including the hydrodynamic limit and classical field theories.

This course is divided into two sections, Part I and Part II.  Part I, found here, provides an introduction to statistical theory. A brief review of probability will be given mainly as background material, however, it is assumed to be known. Topics include normal distribution, limit theorems, Bayesian concepts, and testing, among others. 

Part II prepares students for the remainder of the econometrics sequence and and can be found by visiting 14.381 Fall 2006. 

Statistical Physics in Biology is a survey of problems at the interface of statistical physics and modern biology. Topics include: bioinformatic methods for extracting information content of DNA; gene finding, sequence comparison, and phylogenetic trees; physical interactions responsible for structure of biopolymers; DNA double helix, secondary structure of RNA, and elements of protein folding; considerations of force, motion, and packaging; protein motors, membranes. We also look at collective behavior of biological elements, cellular networks, neural networks, and evolution.

This course explores the theory of self-assembly in surfactant-water (micellar) and surfactant-water-oil (micro-emulsion) systems. It also introduces the theory of polymer solutions, as well as scattering techniques, light, x-ray, and neutron scattering applied to studies of the structure and dynamics of complex liquids, and modern theory of the liquid state relevant to structured (supramolecular) liquids.

The major themes of this course are estimation and control of dynamic systems. Preliminary topics begin with reviews of probability and random variables. Next, classical and state-space descriptions of random processes and their propagation through linear systems are introduced, followed by frequency domain design of filters and compensators. From there, the Kalman filter is employed to estimate the states of dynamic systems. Concluding topics include conditions for stability of the filter equations.

This course examines the fundamentals of detection and estimation for signal processing, communications, and control. Topics covered include: vector spaces of random variables; Bayesian and Neyman-Pearson hypothesis testing; Bayesian and nonrandom parameter estimation; minimum-variance unbiased estimators and the Cramer-Rao bounds; representations for stochastic processes, shaping and whitening filters, and Karhunen-Loeve expansions; and detection and estimation from waveform observations. Advanced topics include: linear prediction and spectral estimation, and Wiener and Kalman filters.

This course is about both the design and execution of human resource management strategies. This course has two central themes: (1) How to think systematically and strategically about aspects of managing the organization's human assets, and (2) What really needs to be done to implement these policies and to achieve competitive advantage. It adopts the perspective of a general manager and addresses human resource topics (including reward systems, performance management, high-performance human resource systems, training and development, recruitment, retention, equal employment opportunity laws, work-force diversity, and union-management relationships) from a strategic perspective.

This course focuses on some of the important current issues in strategic management. It will concentrate on modern analytical approaches and on enduring successful strategic practices. It is consciously designed with a technological and global outlook since this orientation in many ways highlights the significant emerging trends in strategic management. The course is intended to provide the students with a pragmatic approach that will guide the formulation and implementation of corporate, business, and functional strategies.

This course is intended to be an extension of course 15.902, Strategic Management I, with the purpose of allowing the students to experience an in-depth application of the concepts and frameworks of strategic management. Throughout the course, Prof. Arnoldo Hax will discuss the appropriate methodologies, concepts, and tools pertinent to strategic analyses and will illustrate their use by discussing many applications in real-life settings, drawn from his own personal experiences.

This course provides an overview of key concepts in strategic management in the construction, real estate, and architecture industries. Topics include supply chain analysis, market segmentation, vertical integration, competitive advantage, and industry transformation. This course is of interest to students seeking more understanding of the business dynamics of real estate and construction; seeking to provide value in firms which they may join; or seeking to build a foundation for their own entrepreneurial pursuits.

Marketing research may be divided into methods that emphasize understanding "the customer" and methods that emphasize understanding "the market." This course (15.822) deals with the market. The companion course (15.821) deals with the customer.

The course will teach you how to write, conduct and analyze a marketing research survey. The emphasis will be on discovering market structure and segmentation, but you can pursue other project applications.

A major objective of the course is to give you some "hands-on" exposure to analysis techniques that are widely used in consulting and marketing research factor analysis, perceptual mapping, conjoint, and cluster analysis). These techniques used to be considered advanced but now involve just a few keystrokes on most stat software packages.

The course assumes familiarity with basic probability, statistics, and multiple linear regression.

15.320 Strategic Organizational Design focuses on effective organizational design in both traditional and innovative organizations, with special emphasis on innovative organizational forms that can provide strategic advantage. Topics include when to use functional, divisional, or matrix organizations, how IT creates new organizational possibilities, and examples of innovative organizational possibilities, such as democratic decision-making, crowd-based organizations, internal resource markets, and other forms of collective intelligence. Team projects include inventing new possibilities for real organizations.

This string theory course focuses on holographic duality (also known as gauge / gravity duality or AdS / CFT) as a novel method of approaching and connecting a range of diverse subjects, including quantum gravity / black holes, QCD at extreme conditions, exotic condensed matter systems, and quantum information.

In this course we shall develop theoretical methods suitable for the description of the many-body phenomena, such as Hamiltonian second-quantized operator formalism, Greens functions, path integral, functional integral, and the quantum kinetic equation. The concepts to be introduced include, but are not limited to, the random phase approximation, the mean field theory (aka saddle-point, or semiclassical approximation), the tunneling dynamics in imaginary time, instantons, Berry phase, coherent state path integral, renormalization group.

This course uses computer-based methods for the analysis of large-scale structural systems. Topics covered include: modeling strategies for complex structures; application to tall buildings, cable-stayed bridges, and tension structures; introduction to the theory of active structural control; design of classical feedback control systems for civil structures; and simulation studies using customized computer software.

This course covers the fundamental concepts of structural mechanics with applications to marine, civil, and mechanical structures. Topics include analysis of small deflections of beams, moderately large deflections of beams, columns, cables, and shafts; elastic and plastic buckling of columns, thin walled sections and plates; exact and approximate methods; energy methods; principle of virtual work; introduction to failure analysis of structures. We will include examples from civil, mechanical, offshore, and ship structures such as the collision and grounding of ships.