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.

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.

Welcome to the website for An Introduction to Computer Networks, a free and open general-purpose computer-networking textbook, complete with diagrams and exercises. It covers the LAN, internetworking and transport layers, focusing primarily on TCP/IP. Particular attention is paid to congestion; other special topics include queuing, real-time traffic, network management, security and the ns simulator.

The book is suitable as the primary text for an undergraduate or introductory graduate course in computer networking, as a supplemental text for a wide variety of network-related courses, and as a reference work.

An introduction to several fundamental ideas in electrical engineering and computer science, using digital communication systems as the vehicle. The three parts of the course - bits, signals, and packets - cover three corresponding layers of abstraction that form the basis of communication systems like the Internet. The course teaches ideas that are useful in other parts of EECS: abstraction, probabilistic analysis, superposition, time and frequency-domain representations, system design principles and trade-offs, and centralized and distributed algorithms. The course emphasizes connections between theoretical concepts and practice using programming tasks and some experiments with real-world communication channels.

This course will highlight common principles that permeate the functioning of networks and how the same issues related to robustness, fragility and interlinkages arise in several different types of networks. It will both introduce conceptual tools from dynamical systems, random graph models, optimization and game theory, and cover a wide variety of applications.

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.

" 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."

Subject 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 on the importance of the organizational context in influencing which individual styles and skills are effective. Employs a wide variety of learning tools, from experiential learning to the more conventional discussion of written cases. Subject centers on three complementary perspectives on organizations: the strategic design, political, and cultural "lenses" on organizations. Restricted to first-year Sloan master's students.

The course purpose is to provide the substance and skill necessary to make sound business decisions relating to information systems, and to work with senior line managers in the resolution of issues and problems in this area. Categories of issues which will be addressed in the course include: How do IT and its various manifestations in business, such as the Internet, affect current and future competitiveness? How do we align business strategy and plans with IT strategy and IT plans? How can we engage executives in learning and leading IT-related change? How do we implement new systems, change work behavior, manage projects? How should we organize and govern IT in an organization.

Subject assesses the relationships between sequence, structure, and function in complex biological networks as well as progress in realistic modeling of quantitative, comprehensive functional-genomics analyses. Topics include: algorithmic, statistical, database, and simulation approaches; and practical applications to biotechnology, drug discovery, and genetic engineering. Future opportunities and current limitations critically assessed. Problem sets and project emphasize creative, hands-on analyses using these concepts.

This class deals with the modeling and analysis of queueing systems, with applications in communications, manufacturing, computers, call centers, service industries and transportation. Topics include birth-death processes and simple Markovian queues, networks of queues and product form networks, single and multi-server queues, multi-class queueing networks, fluid models, adversarial queueing networks, heavy-traffic theory and diffusion approximations. The course will cover state of the art results which lead to research opportunities.