This course is a graduate level introduction to automatic discourse processing. The emphasis will be on methods and models that have applicability to natural language and speech processing. The class will cover the following topics: discourse structure, models of coherence and cohesion, plan recognition algorithms, and text segmentation. We will study symbolic as well as machine learning methods for discourse analysis. We will also discuss the use of these methods in a variety of applications ranging from dialogue systems to automatic essay writing. This subject qualifies as an Artificial Intelligence and Applications concentration subject.

Wave equations for fluid and visco-elastic media. Wave-theory formulations of acoustic source radiation and seismo-acoustic propagation in stratified ocean waveguides. Wavenumber Integration and Normal Mode methods for propagation in plane-stratified media. Seismo-Acoustic modeling of seabeds and ice covers. Seismic interface and surface waves in a stratified seabed. Parabolic Equation and Coupled Mode approaches to propagation in range-dependent ocean waveguides. Numerical modeling of target scattering and reverberation clutter in ocean waveguides. Ocean ambient noise modeling. Students develop propagation models using all the numerical approaches relevant to state-of-the-art acoustic research.

This course covers the analytical, graphical, and numerical methods supporting the analysis and design of integrated biological systems. Topics include modularity and abstraction in biological systems, mathematical encoding of detailed physical problems, numerical methods for solving the dynamics of continuous and discrete chemical systems, statistics and probability in dynamic systems, applied local and global optimization, simple feedback and control analysis, statistics and probability in pattern recognition.

Introduction to computer graphics hardware, algorithms, and software. Topics include: line generators, affine transformations, line and polygon clipping, splines, interactive techniques, perspective projection, solid modeling, hidden surface algorithms, lighting models, shading, and animation. Substantial programming experience required. 6.837 offers an introduction to computer graphics hardware, algorithms, and software. Topics include: line generators, affine transformations, line and polygon clipping, splines, interactive techniques, perspective projection, solid modeling, hidden surface algorithms, lighting models, shading, and animation. Substantial programming experience is required.

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.

Emphasizes the relationship among technology, hardware organization, and programming systems in the evolution of computer architecture. Pipelined, out-of-order, and speculative execution. Superscaler, VLIW, vector, and multithreaded processors. Addressing structures and virtual memory, and exception handling. I/O and memory systems. Parallel computers; message passing and shared memory systems. Memory models, synchronization, and cache coherence protocols. Vector supercomputers. Assumes an undergraduate knowledge of computer systems.

Introduces abstraction as an important mechanism for problem decomposition and solution formulation in the biomedical domain, and examines computer representation, storage, retrieval, and manipulation of biomedical data. Examines effect of programming paradigm choice on problem-solving approaches, introduces data structures and algorithms. Presents knowledge representation schemes for capturing biomedical domain complexity. Teaches principles of data modeling for efficient storage and retrieval. The final project involves building a medical information system that encompasses the different concepts taught in the subject.

Used for students receiving Advanced Placement credit and transfer credit. Program of study or research to be arranged with a Department faculty member. Written report required. Permission of Department required.

Critical review of works, theories, and polemics in architecture in the aftermath of WWII. Aim is a historical understanding of the period and the development of a meaningful framework to assess contemporary issues in architecture. Special attention paid to historiographic questions of how architects construe the terms of their "present." Required of M.Arch. students.

Thermal backgrounds in space. Cosmological principle and its consequences: Newtonian cosmology and types of "universes"; survey of relativistic cosmology; horizons. Overview of evolution in cosmology; radiation and element synthesis; physical models of the "early stages." Formation of large-scale structure to variability of physical laws. First and last states. Some knowledge of relativity expected. 8.962 recommended though not required. This course provides an overview of astrophysical cosmology with emphasis on the Cosmic Microwave Background (CMB) radiation, galaxies and related phenomena at high redshift, and cosmic structure formation. Additional topics include cosmic inflation, nucleosynthesis and baryosynthesis, quasar (QSO) absorption lines, and gamma-ray bursts. Some background in general relativity is assumed.

A series of presentations on an advanced topic in the field of geology by the visiting William Otis Crosby lecturer. The Crosby lectureship is awarded to a distinguished international scientist each year to introduce new scientific perspectives to the MIT community. Subject content and structure vary from year to year.

This course features a rigorous introduction to modern cryptography, with an emphasis on the fundamental cryptographic primitives of public-key encryption, digital signatures, pseudo-random number generation, and basic protocols and their computational complexity requirements.

" This course covers the following topics: X-ray diffraction: symmetry, space groups, geometry of diffraction, structure factors, phase problem, direct methods, Patterson methods, electron density maps, structure refinement, how to grow good crystals, powder methods, limits of X-ray diffraction methods, and structure data bases."

" This course in crystal structure refinement examines the practical aspects of crystal structure determination from data collection strategies to data reduction and basic and advanced refinement problems of organic and inorganic molecules."

The subject of this course is the historical process by which the meaning of "technology" has been constructed. Although the word itself is traceable to the ancient Greek root teckhne (meaning art), it did not enter the English language until the 17th century, and did not acquire its current meaning until after World War I. The aim of the course, then, is to explore various sectors of industrializing 19th and 20th century Western society and culture with a view to explaining and assessing the emergence of technology as a pivotal word (and concept) in contemporary (especially Anglo-American) thought and expression.

This course examines computers anthropologically, as artifacts revealing the social orders and cultural practices that create them. Students read classic texts in computer science along with cultural analyses of computing history and contemporary configurations. It explores the history of automata, automation and capitalist manufacturing; cybernetics and WWII operations research; artificial intelligence and gendered subjectivity; robots, cyborgs, and artificial life; creation and commoditization of the personal computer; the growth of the Internet as a military, academic, and commercial project; hackers and gamers; technobodies and virtual sociality. Emphasis is placed on how ideas about gender and other social differences shape labor practices, models of cognition, hacking culture, and social media.

This course focuses on Third World development using case studies and team collaboration. Students draw lessons from success stories and identify challenges, unintended consequences and failures in implementing technologies, projects and policies. Students acquire skills in the building of partnerships and learn how to pilot, implement, and scale-up a selected innovation for the common good. Teams develop an idea, project or business plan that is ready to roll by semester's end.

Cellular responses to DNA damage constitute one of the most important fields in cancer biology. In this class we will analyze classical and recent papers from the primary research literature to gain a profound understand of cell cycle regulation and DNA damage checkpoints that act as powerful emergency brakes to prevent cancer. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. Many instructors of the Advanced Undergraduate Seminars are postdoctoral scientists with a strong interest in teaching.

Modeling of the control processes in conventional and high-speed data communication networks. Develops and utilizes elementary concepts from queueing theory, algorithms, linear and nonlinear programming to study the problems of line and network protocols, distributed algorithms, quasi-static and dynamic routing, congestion control, deadlock prevention. Treats local and wide-area networks, and high-speed electronic and optical networks. Focuses on the fundamentals of data communication networks. One goal is to give some insight into the rationale of why networks are structured the way they are today and to understand the issues facing the designers of next-generation data networks. Much of the course focuses on network algorithms and their performance. Students are expected to have a strong mathematical background and an understanding of probability theory. Topics discussed include: layered network architecture, Link Layer protocols, high-speed packet switching, queueing theory, Local Area Networks, and Wide Area Networking issues, including routing and flow control.

Introduces students to a class of methods known as data mining that assists managers in recognizing patterns and making intelligent use of massive amounts of electronic data collected via the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Topics covered: subset selection in regression, collaborative filtering, tree-structured classification and regression, cluster analysis, and neural network methods. Examples of successful applications in areas such as credit ratings, fraud detection, database marketing, customer relationship management, and investments and logistics are covered. Hands-on experimentation with data-mining software is used. Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these repositories. A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments. The field of data mining has evolved from the disciplines of statistics and artificial intelligence. This course will examine methods that have emerged from both fields and proven to be of value in recognizing patterns and making predictions from an applications perspective. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.