This class analyzes complex biological processes from the molecular, cellular, extracellular, and organ levels of hierarchy. Emphasis is placed on the basic biochemical and biophysical principles that govern these processes. Examples of processes to be studied include chemotaxis, the fixation of nitrogen into organic biological molecules, growth factor and hormone mediated signaling cascades, and signaling cascades leading to cell death in response to DNA damage. In each case, the availability of a resource, or the presence of a stimulus, results in some biochemical pathways being turned on while others are turned off. The course examines the dynamic aspects of these processes and details how biochemical mechanistic themes impinge on molecular/cellular/tissue/organ-level functions. Chemical and quantitative views of the interplay of multiple pathways as biological networks are emphasized. Student work will culminate in the preparation of a unique grant application in an area of biological networks.

This class analyzes complex biological processes from the molecular, cellular, extracellular, and organ levels of hierarchy. Emphasis is placed on the basic biochemical and biophysical principles that govern these processes. Examples of processes to be studied include chemotaxis, the fixation of nitrogen into organic biological molecules, growth factor and hormone mediated signaling cascades, and signaling cascades leading to cell death in response to DNA damage. In each case, the availability of a resource, or the presence of a stimulus, results in some biochemical pathways being turned on while others are turned off. The course examines the dynamic aspects of these processes and details how biochemical mechanistic themes impinge on molecular/cellular/tissue/organ-level functions. Chemical and quantitative views of the interplay of multiple pathways as biological networks are emphasized. Student work will culminate in the preparation of a unique grant application in an area of biological networks.

This module offers an introduction to Bayesian networks by means of a worked example of computing a bayesian network from a joint probability distribution (JPD).

Topics on the engineering and analysis of network protocols and architecture, including: architectural principles for designing heterogeneous networks; congestion control; unicast and multicast routing; wireless and mobile networking; network quality of service; router design; network security; streaming and multicast applications; naming; content distribution; and peer-to-peer networking. Readings from original research papers, industry white papers, and Internet RFCs. Semester-long project and paper.

Topics on the engineering of computer software and hardware systems: techniques for controlling complexity; strong modularity using client-server design, virtual memory, and threads; networks; atomicity and coordination of parallel activities; recovery and reliability; privacy, security, and encryption; and impact of computer systems on society. Case studies of working systems and readings from the current literature provide comparisons and contrasts. Two design projects. Students engage in extensive written communication exercises. Enrollment may be limited.

" This course covers topics on the engineering of computer software and hardware systems: techniques for controlling complexity; strong modularity using client-server design, virtual memory, and threads; networks; atomicity and coordination of parallel activities; recovery and reliability; privacy, security, and encryption; and impact of computer systems on society. Case studies of working systems and readings from the current literature provide comparisons and contrasts. Two design projects are required, and students engage in extensive written communication exercises."

On January 23-24, 2006, Intelligent Television hosts the Economics of Open Content symposium at MIT to bring together representatives from media industries, cultural and educational institutions, and legal and business minds to discuss how to make open content happen better and faster.

Topics include Collaboration and the Marketplace, New Models of Creative Production in the Digital Age, Keynote Address: Openness as an Ethos, The Wealth of Networks, The Economics of Knowledge as a Public Good, The Economics of Open Courseware, The Economics of Open Text, Convergence Culture: Consumer Participation and the Economics of Mass Media, The Economics of the Music Industry, If Only We Knew Yesterday What We Know Today, The Economics of Open Archives, Museums, and Libraries, The Economics of the Public Domain, The Economics of Film and Television, The New Economics of Gaming, Everything is Miscellaneous, Business Interests in Open Content, Next Steps: Cooperation Across Institutions and Industries

Subject covers technology concepts and trends underlying current and future developments in information technology, and fundamental principles for the effective use of computer-based information systems. Special emphasis on networks and distributed computing, including the web. Other topics include: hardware and operating systems, software development tools and processes, relational databases, security and cryptography, enterprise applications and business process redesign, and electronic commerce. Hands-on exposure to Web, database, and graphical user interface (GUI) tools. Primarily for Sloan master's students.

Broad coverage of technology concepts underlying modern computing and information management. Topics include computer architecture and operating systems, relational database systems, graphical user interfaces, networks, client/server systems, enterprise applications, cryptography, and the web. Hands-on exposure to internet services, Microsoft Access database management system, and Lotus Notes. Information Technology I helps students understand technical concepts underlying current and future developments in information technology. There will be a special emphasis on networks and distributed computing. Students will also gain some hands-on exposure to powerful, high-level tools for making computers do amazing things, without the need for conventional programming languages. Since 15.564 is an introductory course, no knowledge of how computers work or are programmed is assumed.

Focus on the key role that information technology plays in enabling organizational change and integration, especially in manufacturing. Topics include: trends in core technologies, including computer hardware, software, communications, and networks; the development and evolution of the internet and web; business models for electronic commerce; reinventing business processes and supply chain management; evaluating and managing the use of advanced information technologies in manufacturing; and new technology-enabled forms of working and organizing. In virtually every industry and every firm, information technology is driving change, creating opportunities and challenges. Leaders who don't understand at least the fundamentals of information systems will be at a strategic disadvantage. This course provides broad coverage of technology concepts and trends underlying current and future developments in information technology, and fundamental principles for the effective use of computer-based information systems. There will be a special emphasis on manufacturing. Information Systems topics that will be covered include networks and distributed computing, including the World Wide Web, hardware and operating systems, software development tools and processes, relational databases, security and cryptography, enterprise applications, B2B, the semantic web and electronic commerce. Sloan LFM students with an interest in Information Systems are encouraged to register for this course.

You will investigate the effects of windowing and zero-padding on the Discrete Fourier Transform of a signal, as well as the effects of data-set quantities and weighting windows used in Power Spectral Density estimation.

Basic subject on matrix theory and linear algebra, emphasizing topics useful in other disciplines, including systems of equations, vector spaces, determinants, eigenvalues, similarity, and positive definite matrices. Applications to least-squares approximations, stability of differential equations, networks, Fourier transforms, and Markov processes. Uses MATLAB. Compared with 18.700, more emphasis on matrix algorithms and many applications.

This is a basic subject on matrix theory and linear algebra. Emphasis is given to topics that will be useful in other disciplines, including systems of equations, vector spaces, determinants, eigenvalues, similarity, and positive definite matrices.

Management Information Systems (MIS) is a formal discipline within business education that bridges the gap between computer science and the well-known business disciplines of finance, marketing, and management. In its most general terms, information systems encompass any interactions between organized data and people. MIS can be the means by which information is transmitted (such as the Internet), the software that displays the information (such as Microsoft Excel), or the systems that manage the data. In this course, the student will learn about the various components of information systems and how to leverage them in business. Upon successful completion of this course, students will be able to: Describe the use and function of Management Information Systems; Describe and evaluate Information Systems Development processes and techniques; Identify and evaluate hardware and software requirements for Information Systems; Evaluate Data Management technologies; Explain the security risks associated with Management Information Systems. (Business Administration 206)

Review of linear algebra, applications to networks, structures, and estimation, Lagrange multipliers, differential equations of equilibrium, Laplace's equation and potential flow, boundary-value problems, minimum principles and calculus of variations, Fourier series, discrete Fourier transform, convolution, applications.

This course focuses on Object-Oriented Programming and programming techniques both at the application layer and the transport layer of the TCP/IP protocol stack. Additional concepts covered include text transport (moving text from one computer to another over the network), data transport, object transport, remote function calls, and, finally, class transport. Upon successful completion of this course, the student will be able to: code solutions using the fundamental concepts of object-oriented programming in Java; compare and contrast the usage of the Java APIs for each of the different network protocols with emphasis on the application and transport layers; explain how consistency and reusability are achieved in network processing from input through output; complete a variety of introductory to complex network programming tasks utilizing Java; solve problems that use XML to activate Java; compare and contrast object sharing with Java RMI and SOAP. This free course may be completed online at any time. (Computer Science 407)

The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory. It starts with a review of classical statistical techniques, including Regularization Theory in RKHS for multivariate function approximation from sparse data. Next, VC theory is discussed in detail and used to justify classification and regression techniques such as Regularization Networks and Support Vector Machines. Selected topics such as boosting, feature selection and multiclass classification will complete the theory part of the course. During the course we will examine applications of several learning techniques in areas such as computer vision, computer graphics, database search and time-series analysis and prediction. We will briefly discuss implications of learning theories for how the brain may learn from experience, focusing on the neurobiology of object recognition. We plan to emphasize hands-on applications and exercises, paralleling the rapidly increasing practical uses of the techniques described in the subject.

Neural networks are a different paradigm for computing based on the parallel architecture of animal brains.

" 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."