Computer Science

6.801 Machine Vision

()
Prereq: 6.003 or permission of instructor
Units: 3-0-9
Lecture: TR2.30-4 (4-149)

Deriving a symbolic description of the environment from an image. Understanding physics of image formation. Image analysis as an inversion problem. Binary image processing and filtering of images as preprocessing steps. Recovering shape, lightness, orientation, and motion. Using constraints to reduce the ambiguity. Photometric stereo and extended Gaussian sphere. Applications to robotics; intelligent interaction of machines with their environment.
B. K. P. Horn

6.803 The Human Intelligence Enterprise

()
(Subject meets with 6.833)
Prereq: 6.034 or permission of instructor
Units: 3-0-9

Analyzes seminal work directed at the development of a computational understanding of human intelligence, such as work on object tracking, object recognition, change representation, language evolution, and the role of symbols in learning and communication. Reviews visionary ideas of Turing, Minsky, and other influential thinkers. Examines the role of brain scanning, systems neuroscience, and cognitive psychology. Emphasis on discussion and analysis of original papers. Meets with 6.833 but assignments differ. Enrollment limited.
P. H. Winston

6.804J Computational Cognitive Science

()
(Same subject as 9.66J)
(Subject meets with 9.660)
Prereq: 9.07, 18.05, 6.041, or permission of instructor
Units: 3-0-9
Lecture: TR3.30-5 (46-1015) Recitation: M EVE (7.30 PM) (14-0637)

Introduction to computational theories of human cognition. Focus on principles of inductive learning and inference, and the representation of knowledge. Computational frameworks covered include Bayesian and hierarchical Bayesian models; probabilistic graphical models; nonparametric statistical models and the Bayesian Occam?s razor; sampling algorithms for approximate learning and inference; and probabilistic models defined over structured representations such as first-order logic, grammars, or relational schemas. Applications to understanding core aspects of cognition, such as concept learning and categorization, causal reasoning, theory formation, language acquisition, and social inference. Graduate students complete a final project.
J. B. Tenenbaum

6.805 Ethics and the Law on the Electronic Frontier

()
(Subject meets with STS.085, STS.487)
Prereq: Permission of instructor
Units: 3-0-9
URL: http://www-swiss.ai.mit.edu/6.805/
Lecture: R2-5 (4-231)

Studies the growth of computer and communications technology and the new legal and ethical challenges that reflect tensions between individual rights and societal needs. Topics: computer crime; intellectual property restrictions on software; encryption, privacy, and national security; academic freedom and free speech. Students meet and question technologists, activists, law enforcement agents, journalists, and legal experts. Extensive use of the web for readings and other materials. Students engage in extensive written and oral communication exercises. STS.085 meets with 6.805 and carries HASS credit. 6.805 may be used as an Engineering Concentration Elective. Enrollment limited.
H. Abelson, M. Fischer

6.807 Computational Functional Genomics

()
(Subject meets with 6.874J, 7.90J)
Prereq: 7.012, 7.013, 7.014, or 7.015
Units: 3-0-9

Study and discussion of computational approaches and algorithms for contemporary problems in functional genomics. Topics include biological complexity, genome structure and function, high-throughput experimental data, data normalization, data representation, gene clustering, statistical network models, continuous dynamic models, statistical metrics for model validation, model elaboration, experiment planning, and the computational complexity of functional genomics problems. Meets with 6.874J, but assignments differ.
D. K. Gifford, T. S. Jaakkola

6.821 Programming Languages

()
Prereq: Permission of instructor
Units: 4-0-8
Lecture: TR1-2.30 (32-124)

Principles of functional, imperative, and logic programming languages. Meta-circular interpreters, semantics (operational and denotational), type systems (polymorphism, inference, and abstract types), object oriented programming, modules, and multiprocessing. Case studies of contemporary programming languages. Programming experience and background in language implementation required.
D. K. Gifford

6.823 Computer System Architecture

()
Prereq: 6.004
Units: 4-0-8
Lecture: MW1-2.30 (32-124) Recitation: F1 (32-144)

Emphasizes the relationship among technology, hardware organization, and programming systems in the evolution of computer architecture. Pipelined, out-of-order, and speculative execution. Superscalar, VLIW, vector, and multithreaded processors. 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. Embedded computers. Assumes an undergraduate knowledge of computer systems. 4 Engineering Design Points.
Arvind, K. Asanovic

6.824 Distributed Computer Systems Engineering

()
Prereq: 6.033, programming experience with C/C++
Units: 3-0-9
Lecture: TR11-12.30 (32-144) +final

Abstractions and implementation techniques for design of distributed systems; server design, network programming, naming, storage systems, security, and fault tolerance. Readings from current literature. 6 Engineering Design Points. Enrollment limited.
R. T. Morris, M. F. Kaashoek

6.825 Techniques in Artificial Intelligence

()
Prereq: 6.041, 6.042J (6.046J and 6.034 desirable)
Units: 3-0-9

A graduate-level introduction to artificial intelligence. Topics include representation and inference in first-order logic; modern deterministic and decision-theoretic planning techniques; basic supervised learning methods; and Bayesian network inference and learning.
L. Kaelbling

6.826 Principles of Computer Systems

()
Prereq: Permission of instructor
Units: 3-0-9
URL: http://web.mit.edu/6.826/www/

An introduction to the basic principles of computer systems with emphasis on the use of rigorous techniques as an aid to understanding and building modern computing systems. Particular attention paid to concurrent and distributed systems. Topics include: specification and verification, concurrent algorithms, synchronization, naming, Networking, replication techniques (including distributed cache management), and principles and algorithms for achieving reliability. Alternate years.
B. W. Lampson

6.827 Multithreaded Parallelism: Languages and Compilers

()
Prereq: 6.001, 6.042J
Units: 3-0-9
URL: http://www.csg.lcs.mit.edu/6.827/

Languages and compilers to exploit multithreaded parallelism. Implicit parallel programming using functional languages and their extensions. Higher-order functions, non-strictness, and polymorphism. Explicit parallel programming and nondeterminism. The lambda calculus and its variants. Term rewriting and operational semantics. Compiling multithreaded code for symmetric multiprocessors and clusters. Static analysis and compiler optimizations. Alternate years. 4 Engineering Design Points.
Arvind

6.828 Operating System Engineering

()
Prereq: 6.033, 6.170
Units: 3-6-3
Lecture: MW12.30-2 (32-144) +final

Fundamental design and implementation issues in the engineering of operating systems. Lectures based on the study of a symmetric multiprocessor version of UNIX version 6 and research papers. Topics include virtual memory; file system; threads; context switches; kernels; interrupts; system calls; interprocess communication; coordination, and interaction between software and hardware. Individual laboratory assignments accumulate in the construction of a minimal operating system (for an x86-based personal computer) that implements the basic operating system abstractions and a shell. Knowledge of programming in the C language is a prerequisite. 6 Engineering Design Points.
M. F. Kaashoek

6.829 Computer Networks

()
Prereq: 6.033 or permission of instructor
Units: 4-0-8

Topics on the engineering and analysis of network protocols and architecture, including architectural principles for designing heterogeneous networks; transport protocols; internet routing foundations and practice; router design; congestion control and network resource management; wireless networks; network security; naming; overlay and peer-to-peer networks. Readings from original research papers and Internet RFCs. Semester-long project and paper. Enrollment may be limited. 4 Engineering Design Points.
H. Balakrishnan

6.830 Database Systems

()
Prereq: 6.033; 6.046J or 6.006; or permission of instructor
Units: 3-0-9
Lecture: TR1-2.30 (32-155)

Topics related to the engineering and design of database systems, including: data models; database and schema design; schema normalization and integrity constraints; query processing; query optimization and cost estimation; transactions; recovery; concurrency control; isolation and consistency; distributed, parallel, and heterogeneous databases; adaptive databases; trigger systems; pub-sub systems; semi structured data and XML querying. Lecture and readings from original research papers. Semester-long project and paper. Enrollment may be limited. 4 Engineering Design Points.
S. R. Madden

6.831 User Interface Design and Implementation

()
Prereq: 6.170 or permission of instructor
Units: 3-0-9
URL: http://groups.csail.mit.edu/uid/6.831/

Design, implementation, and evaluation of human-computer interfaces. Human capabilities, including the human information processing model, perception, Fitts's Law, memory, attention, and color vision; task analysis, user-centered design, design principles; low-fidelity prototyping; heuristic evaluation, formative evaluation, controlled experiments; model-view-controller, input models, output models, constraints, layout, animation, and automatic user interface generation. Readings from current literature, short assignments, and substantial group programming project. 6 Engineering Design Points.
R. C. Miller

6.833 The Human Intelligence Enterprise

()
(Subject meets with 6.803)
Prereq: 6.034
Units: 3-0-9

Meets with undergraduate subject 6.803. Intended, in part, to prepare students for MEng thesis work in the Artificial Intelligence concentration. Requires completion of supplementary exercises and a substantial term project. Enrollment limited.
P. H. Winston

6.834J Cognitive Robotics

()
(Same subject as 16.412J)
Prereq: 6.041 or 6.042; and 16.410, 16.413, 6.034, or 6.825
Units: 3-0-9

Algorithms and paradigms for creating a wide range of robotic systems that act intelligently and robustly, by reasoning extensively from models of themselves and their world. Examples range from autonomous Mars explorers and cooperative air vehicles, to everyday embedded devices. Topics include deduction and search in real-time; temporal, decision-theoretic and contingency planning; dynamic execution and re-planning; reasoning about hidden state and failures; reasoning under uncertainty, path planning, mapping and localization, and cooperative and distributed robotics.
B. C. Williams, R. Davis

6.835 Digital and Computational Photography
(New)

()
(Subject meets with 6.865)
Prereq: 18.06 and 6.003
Units: 3-0-9

Computational photography is a new field at the convergence of photography, computer vision, image processing, and computer graphics. Fundamentals and applications of hardware and software techniques, with an emphasis on software methods. Provides sufficient background to implement new solutions to photography challenges and opportunities. Topics include cameras and image formation, image processing and image representations, high-dynamic-range-imaging, human visual perception and color, single view 3-D model reconstruction, morphing, data-rich photography, Super-resolution, image-based rendering. 6 Engineering Design Points.
F. P. Durand, W. T. Freeman

6.837 Computer Graphics

()
Prereq: 18.02, 6.170 or permission of instructor
Units: 3-0-9
URL: http://courses.csail.mit.edu/6.837/
Lecture: TR2.30-4 (32-144) +final

Introduction to computer graphics algorithms, software and hardware. Topics include ray tracing, the graphics pipeline, transformations, texture mapping, shadows, sampling, global illumination, splines, animation and color. 6 Engineering Design Points.
F. Durand, J. Popovic

6.838 Advanced Topics in Computer Graphics

()
Prereq: 6.837
Units: 3-0-9
URL: https://ocw-int.mit.edu/6/6.838/f02/index.html

In-depth study of an active research topic in computer graphics. Topics change each term. Readings from the literature, student presentations, short assignments, and a programming project.
F. P. Durand, J. Popovic

6.839 Advanced Computer Graphics

()
Prereq: 18.06, 6.046J, 6.837 or permission of instructor
Units: 3-0-9

A graduate level course investigates computational problems in rendering, animation, and geometric modeling. The course draws on advanced techniques from computational geometry, applied mathematics, statistics, scientific computing and other. Substantial programming experience required.
J. Popovic, F. Durand

6.840J Theory of Computation

() (H except 18)
(Same subject as 18.404J)
Prereq: 18.310 or 18.062J
Units: 4-0-8
URL: http://www-math.mit.edu/~sipser/18404/18.404.html
Lecture: TR11-12.30 (2-190) Recitation: F11 (2-147) or F1 (2-147) or F2 (2-147) +final

A more extensive and theoretical treatment of the material in 6.045J/18.400J, emphasizing computability and computational complexity theory. Regular and context-free languages. Decidable and undecidable problems, reducibility, recursive function theory. Time and space measures on computation, completeness, hierarchy theorems, inherently complex problems, oracles, probabilistic computation, and interactive proof systems.
M. Sipser

6.841J Advanced Complexity Theory

()
(Same subject as 18.405J)
Prereq: 6.840J/18.404J
Units: 3-0-9
URL: http://theory.csail.mit.edu/~madhu/ST07/

Current research topics in computational complexity theory. Nondeterministic, alternating, probabilistic, and parallel computation models. Boolean circuits. Complexity classes and complete sets. The polynomial-time hierarchy. Interactive proof systems. Relativization. Definitions of randomness. Pseudo-randomness and derandomizations. Interactive proof systems and probabilistically checkable proofs.
Information: M. Sudan, M. X. Goemans

6.844 Computability Theory of and with Scheme

()
Prereq: 6.001, 6.042J or comparable mathematical maturity
Units: 5-0-7

Theory for programmers. Introduction to programming and computability theory based on a "substitution" model of computation by Scheme programs with side effects. Computation as algebraic manipulation: provable and valid inequalities for multivariate polynomials. Scheme evaluation as algebraic manipulation and term rewriting. Paradoxes from self-application and introduction to formal programming semantics. Undecidability of the Halting Problem for Scheme. Properties of recursively enumerable sets, leading to Incompleteness theorems for Scheme equivalences. Introduction to logic for program specification and verification. Hilbert's tenth problem. Alternate years.
A. R. Meyer

6.846 Parallel Processing: Architecture and Applications

()
Prereq: 6.823 or permission of instructor
Units: 3-0-9
Subject Cancelled

Principles in the design and programming of parallel machines. Continuum, particle, and graph applications. Communication and synchronization. Locality in parallel computations. Computational models and parallel computers: dataflow, shared memory, data parallel, and message passing. Parallel machine mechanisms such as full-empty synchronization bits, cache coherence, and multithreading. Design and performance evaluation of multiprocessor systems. Compilation and runtime technologies: partitioning, placement, and scheduling. Substantial project required. 4 Engineering Design Points.
A. Agarwal

6.850 Geometric Computing

()
Prereq: 6.046J
Units: 3-0-9

Introduction to the design and analysis of algorithms for geometric problems, in low and high dimensional spaces. Algorithms: convex hulls, polygon triangulation, Delaunay triangulation, motion planning, pattern matching. Geometric data structures: point location, Voronoi diagrams, Binary Space Partitions. Geometric problems in higher dimensions: linear programming, closest pair problems. High dimensional nearest neighbor search and low-distortion embeddings between metric spaces. Geometric algorithms for massive data sets: external memory and streaming algorithms. Geometric optimization.
P. Indyk

6.851 Advanced Data Structures

()
Prereq: 6.046J
Units: 3-0-9
URL: http://courses.csail.mit.edu/6.851/

More advanced and powerful data structures for answering several queries on the same data. Such structures are crucial in particular for designing efficient algorithms. Dictionaries; hashing; search trees. Self-organizing data structures; linear search; splay trees; dynamic optimality. Predecessor problem; van Emde Boas priority queues; y-fast trees. Word-level parallelism; fusion trees; transdichotomous RAM; RAMBO. Strings; text indexing; suffix arrays; suffix trees; compression. Static data structures; compact arrays; rank and select. Succinct data structures; tree encodings; implicit data structures. External-memory data structures; B-trees; buffer trees; cache-oblivious data structures; tree layout. Ordered-file maintenance; order queries in lists.
E. D. Demaine

6.852J Distributed Algorithms

()
(Same subject as 18.437J)
Prereq: 6.046J
Units: 3-0-9
URL: http://theory.csail.mit.edu/classes/6.852/
Subject Cancelled

Design and analysis of concurrent algorithms, emphasizing those suitable for use in distributed networks. Process synchronization, allocation of computational resources, distributed consensus, distributed graph algorithms, election of a leader in a network, distributed termination, deadlock detection, concurrency control, communication, and clock synchronization. Special consideration given to issues of efficiency and fault tolerance. Formal models and proof methods for distributed computation. Alternate years.
N. A. Lynch

6.854J Advanced Algorithms

()
(Same subject as 18.415J)
Prereq: 6.041 or 6.042J; 6.046J
Units: 5-0-7
URL: http://theory.lcs.mit.edu/classes/6.854/
Lecture: MWF2.30-4 (32-144)

First-year graduate subject in algorithms. Emphasizes fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. Surveys a variety of computational models and the algorithms for them. Data structures, network flows, linear programming, computational geometry, approximation algorithms, online algorithms, parallel algorithms, external memory, streaming algorithms.
D. R. Karger

6.855J Network Optimization

()
(Same subject as 15.082J, ESD.78J)
Prereq: 6.046J, 6.251J, 15.081J, or permission of instructor
Units: 3-0-9

Network models for industrial logistics systems, transportation systems, communication systems, and other applications. Emphasizes a rigorous treatment of algorithms and their efficiency?algorithms for shortest routes, maximum flows, minimum cost flows, traffic equilibrium, and network design. Implementation issues.
A. S. Schulz

6.856J Randomized Algorithms

()
(Same subject as 18.416J)
Prereq: 6.854J, 6.041 or 6.042J
Units: 5-0-7

Studies how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Models of randomized computation. Data structures: hash tables, and skip lists. Graph algorithms: minimum spanning trees, shortest paths, and minimum cuts. Geometric algorithms: convex hulls, linear programming in fixed or arbitrary dimension. Approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms. Alternate years.
D. R. Karger

6.857 Network and Computer Security

()
Prereq: 6.033, 6.042J
Units: 3-0-9
URL: http://web.mit.edu/6.857/www/

Techniques for achieving security in multi-user computer systems and distributed computer systems. Topics: physical security; discretionary and mandatory access control; biometrics; information-flow models of security; covert channels; elementary cryptography; public-key cryptography; logic of authentication; electronic cash; viruses; firewalls; electronic voting; risk assessment; secure web browsers.
R. L. Rivest

6.859J Integer Programming and Combinatorial Optimization

()
(Same subject as 15.083J)
Prereq: 15.081J or permission of instructor
Units: 3-0-9
Lecture: TR1-2.30 (5-134)

In-depth treatment of the modern theory of integer programming and combinatorial optimization, emphasizing geometry, duality and algorithms. Topics include formulating problems in integer variables, enhancement of formulations, ideal formulations, integer programming duality, linear and semidefinite relaxations, lattices and their applications, the geometry of integer programming, primal methods, cutting plane methods, connections with algebraic geometry, computational complexity, approximation algorithms, heuristic and enumerative algorithms, mixed integer programming and solutions of large scale problems. Alternate years.
D. J. Bertsimas, A. S. Schulz

6.863J Natural Language and the Computer Representation of Knowledge

()
(Same subject as 9.611J)
Prereq: 6.034
Units: 3-3-6

Relationship between computer representation of knowledge and the structure of natural language. Emphasizes development of the analytical skills necessary to judge the computational implications of grammatical formalisms, and uses concrete examples to illustrate particular computational issues. Efficient parsing algorithms for context-free grammars; augmented transition network grammars. Question answering systems. Extensive laboratory work on building natural language processing systems. 8 Engineering Design Points.
R. C. Berwick

6.864 Advanced Natural Language Processing

()
Prereq: 6.046J or permission of instructor
Units: 3-0-9
Lecture: TR1-2.30 (32-144) +final

Graduate introduction to natural language processing, the study of human language from a computational perspective. Syntactic, semantic and discourse processing models. Emphasis on machine learning or corpus-based methods and algorithms. Use of these methods and models in applications including syntactic parsing, information extraction, statistical machine translation, dialogue systems, and summarization.
R. A. Barzilay, M. J. Collins

6.865 Advanced Computational Photography
(New)

()
(Subject meets with 6.835)
Prereq: 6.003 and 18.06
Units: 3-0-9

Requires the completion of additional advanced homework assignments and presentation of a research paper. See subject description under 6.835.
F. P. Durand, W. T. Freeman

6.866 Machine Vision

()
Prereq: 6.003 or permission of instructor
Units: 3-0-9
Lecture: TR2.30-4 (4-149)

Intensive introduction to the process of generating a symbolic description of the environment from an image. Students expected to attend the 6.801 lectures as well as occasional seminar meetings on special topics. Material presented in 6.801 is supplemented by reading from the literature. Students required to prepare a paper analyzing research in a selected area.
B. K. P. Horn

6.867 Machine Learning

()
Prereq: 6.034, 18.06, 6.041 or 18.05
Units: 3-0-9
Lecture: MW1-2.30 (54-100) Recitation: F10 (26-210) or F11 (26-210) or F2 (26-328) or F3 (26-328)

Principles, techniques, and algorithms in machine learning from the point of view of statistical inference; representation, generalization, and model selection; and methods such as linear/additive models, active learning, boosting, support vector machines, hidden Markov models, and Bayesian networks.
T. Jaakkola, L. P. Kaelbling, M. J. Collins

6.868J The Society of Mind

()
(Same subject as MAS.731J)
Prereq: Must have read The Society of Mind, permission of instructor
Units: 2-0-10
URL: http://web.media.mit.edu/~dustin/6.868/

Introduction to a theory that tries to explain how minds are made from collections of simpler processes. Treats such aspects of thinking as vision, language, learning, reasoning, memory, consciousness, ideals, emotions, and personality. Incorporates ideas from psychology, artificial intelligence, and computer science to resolve theoretical issues such as wholes vs. parts, structural vs. functional descriptions, declarative vs. procedural representations, symbolic vs. connectionist models, and logical vs. common-sense theories of learning. Enrollment limited.
M. Minsky

6.869 Advances in Computer Vision

()
Prereq: 6.801
Units: 3-0-9

Advanced topics in mid- and high-level computer vision with a focus on the use of machine learning techniques and applications in graphics and human-computer interface. Topics may include: image representations, texture models, structure-from-motion algorithms, Bayesian techniques, projective geometrey, object recognition, tracking, shape modeling, and image databases. Applications may include face recognition, multimodal interaction, interacive systems, cinematic special effects, and photorealistic rendering. Covers topics complementary to 6.801/866 and these subjects may be taken in sequence. 6.801/866 or some familiarity with low-level vision topics including image formation, stereo, and motion processing is presumed.
T. Darrell, W. T. Freeman

6.870 Advanced Topics in Computer Vision

()
Prereq: 6.801/6.866 or 6.869 or permission of instructor
Units: 3-0-9

Seminar exploring advanced research topics in the field of computer vision; focus varies with lecturer. Typically structured around discussion of assigned research papers and presentations by students. Example research areas explored in this seminar include learning in vision, computational imaging techniques, multimodal human-computer interaction, biomedical imaging, representation and estimation methods used in modern computer vision.
T. J. Darrell, W. T. Freeman, P. Golland, B. K. P. Horn

6.871 Knowledge-Based Applications Systems

()
Prereq: 6.034
Units: 3-0-9
URL: http://www.ai.mit.edu/courses/6.871/

Development of programs containing a significant amount of knowledge about their application domain. Outline: brief review of relevant AI techniques; case studies from a number of application domains, chosen to illustrate principles of system development; discussion of technical issues encountered in building a system, including selection of knowledge representation and knowledge acquisition, and discussion of current and future research. Experience in building an expert system (term project). 8 Engineering Design Points.
R. Davis, H. E. Shrobe

6.872J Biomedical Computing

()
(Same subject as HST.950J)
Prereq: 6.034
Units: 3-0-9
URL: http://www.chip.org/teaching/hst950/
Lecture: TR9.30-11 (32-144)

Analyzes computational needs of clinical medicine, reviews systems and approaches that have been used to support those needs, and the relationship between clinical data and gene and protein measurements. Topics: the nature of clinical data; architecture and design of healthcare information systems; privacy and security issues; medical expert systems; introduction to bioinformatics. Case studies and guest lectures describe contemporary systems and research projects. Term project using large clinical and genomic data sets integrates classroom topics. 6 Engineering Design Points.
P. Szolovits, I. Kohane, L. Ohno-Machado

6.873J Biomedical Decision Support

()
(Same subject as HST.951J)
Prereq: 6.034 or HST.947; programming skills or permission of instructor
Units: 3-0-9
URL: http://dsg.harvard.edu/courses/hst951/
Lecture: MW9.30-11 (32-144)

Presents the main concepts of decision analysis, artificial intelligence, and predictive model construction and evaluation in the specific context of medical applications. Emphasizes the advantages and disadvantages of using these methods in real-world systems. Technical focus on decision analysis, knowledge-based systems (qualitative and quantitative), learning systems (including logistic regression, classification trees, neural networks), and techniques to evaluate the performance of such systems. Students produce a final project using the methods learned in the subject, based on actual clinical data. Required for students in the master's program in medical informatics, but open to other graduate students and advanced undergraduates.
L. Ohno-Machado, P. Szolovits, S. Vinterbo

6.874J Computational Functional Genomics

()
(Same subject as 7.90J)
(Subject meets with 6.807)
Prereq: 7.012, 7.013, 7.014, or 7.015
Units: 3-0-9
URL: http://web.mit.edu/7.90j/

Focuses on casting contemporary problems in systems biology and functional genomics in computational terms and providing appropriate tools and methods to solve them. Topics include genome structure and function, transcriptional regulation, and stem cell biology in particular; measurement technologies such as microarrays (expression, protein-DNA interactions, chromatin structure); statistical data analysis, predictive and causal inference, and experiment design. Emphasis on coupling problem structures (biological questions) with appropriate computational approaches.
D. K. Gifford, T. S. Jaakkola

6.875J Cryptography and Cryptanalysis

()
(Same subject as 18.425J)
Prereq: 6.046J
Units: 3-0-9

A rigorous introduction to modern cryptography. Emphasis on the fundamental cryptographic primitives of public-key encryption, digital signatures, pseudo-random number generation, and basic protocols and their computational complexity requirements.
S. Goldwasser

6.876J Advanced Topics in Cryptography

()
(Same subject as 18.426J)
Prereq: 6.875J/18.425J
Units: 3-0-9

Recent results in cryptography and interactive proofs. Lectures by instructor, invited speakers, and students. Alternate years.
S. Goldwasser

6.877J Computational Evolutionary Biology

()
(Same subject as HST.949J)
Prereq: 6.046J, 6.047, 7.36, 6.807, or HST.508; or permission of instructor
Units: 3-3-6
Lecture: M2-5 (1-135)

Explores and illustrates theory underlying computational approaches to solving problems in evolutionary biology. Begins with components of evolutionary theory and inferential logic of evolution by natural selection. Emphasizes development of analytical skills needed to judge the computational and algorithmic implications and requirements of evolutionary models. Examples drawn from current research in evolutionary biology: whole-genome species comparison, phylogenetic tree construction, molecular evolution, homology and development, optimization and evolvability, heritability, disease evolution, detecting selection in human populations, and evolution of language. Extensive laboratory exercises in model-building and analyzing evolutionary data. Alternate years. 4 Engineering Design Points.
R. C. Berwick

6.878 Advanced Computational Biology: Genomes, Networks, Evolution

()
(Subject meets with 6.047)
Prereq: 6.001; 7.012; 18.440 or 6.041
Units: 3-0-9
Lecture: TR11-12.30 (2-105)

See description for 6.047. Additionally examines recent publications in the areas covered, with research-style assignments. A more substantial final project is expected, which can lead to a thesis and publication.
M. Kellis, P. Indyk

6.881-6.885 Special Subjects in Computer Science

(, )
Prereq: Permission of instructor
Units arranged
URL: http://www.eecs.mit.edu/current/catalog/index.html
6.885: Lecture: MW11-12.30 (2-139)

Opportunity for group study of advanced subjects related to Computer Science not otherwise included in curriculum. Offerings initiated by members of the EECS faculty on an ad hoc basis, subject to department approval.
G. V. Verghese

6.891-6.899 Special Subjects in Computer Science

(, )
Prereq: Permission of instructor
Units arranged
URL: http://www.eecs.mit.edu/current/catalog/index.html
6.895: Lecture: MW1-2.30 (37-212)
6.897: Lecture: TR1-2.30 (24-121)
6.898: Lecture: W3 (36-153)
6.899: Lecture: F3-6 (56-154)

Opportunity for group study of advanced subjects related to computer science not otherwise included in curriculum. Offerings initiated by members of the EECS faculty on an ad hoc basis, subject to department approval.
G. C. Verghese

Special Subjects

6.901 Inventions and Patents

()
Engineering School-Wide Elective Subject.
(Offered under: 3.172, 6.901, 16.652)
Prereq: 14.02
Units: 3-0-6
URL: http://www.mit.edu:8001/courses/6.901/patent.html
Lecture: M EVE (7-10 PM) (37-212)

History of private and public rights in scientific discoveries and applied engineering, leading to the development of worldwide patent systems. The classes of invention protectable under the patent laws of the US, including the procedures in protecting inventions in the Patent Office and the courts. Reviews of past cases involving inventions and patents in a) the chemical process industry and medical pharmaceutical, biological, and genetic-engineering fields; b) devices in the mechanical, ocean exploration, civil, and/or aeronautical fields; c) the electrical, computer, software, and electronic areas, including key radio, solid-state, computer and software inventions; and also d) software protection afforded under copyright laws. Conducting periodic joint real-time class sessions and discussions by video-audio Internet conferencing, with other universities. Enrollment limited.
R. H. Rines

6.910?6.914 Special Studies in Electrical Engineering and Computer Science

(, , )
Prereq: Permission of instructor
Units arranged [P/D/F]
6.911: Consult department
6.912: Consult department
6.913: Consult department
6.914: Consult department

6.915?6.919 Special Advanced Undergraduate Subjects in Electrical Engineering and Computer Science

(, , )
Prereq: Permission of instructor
Units arranged
6.915: Consult department
6.916: Consult department
6.917: Consult department
6.918: Consult department
6.919: Consult department

Advanced subjects not offered in the regular curriculum. Consult department to learn of offerings for a particular term.
A. C. Smith

6.920 Practical Work Experience

(, , , )
Prereq: --
Units: 0-1-0 [P/D/F]
Consult department

For Course 6 students participating in off-campus work experiences in electrical engineering or computer science. Before enrolling, students must have an employment offer from a company or organization and must find an EECS supervisor. Upon completion of the work, student must submit a letter from the employer describing the work accomplished, along with a substantive final report from the student approved by the MIT supervisor. Subject to departmental approval. Consult departmental undergraduate office.
A. C. Smith

6.921 VI-A Internship

()
Prereq: --
Units: 0-12-0 [P/D/F]

Provides academic credit for the first assignment of VI-A undergraduate students at companies affiliated with the department's VI-A internship program. Enrollment limited to students participating in the VI-A internship program.
M. Zahn

6.922 Advanced VI-A Internship

(, )
Prereq: 6.921
Units: 0-12-0 [P/D/F]

Provides academic credit for the second assignment of VI-A undergraduate students at companies affiliated with the department's VI-A internship program. Enrollment limited to students participating in the VI-A internship program.
M. Zahn

6.923 Pre-Graduate VI-A Internship

(, )
Prereq: 6.922
Units: 0-12-0 [P/D/F]

Provides academic credit for the third assignment of VI-A undergraduate students at companies affiliated with the department's VI-A internship program. Enrollment limited to students participating in the VI-A internship program.
M. Zahn

6.930 Management in Engineering

()
Engineering School-Wide Elective Subject.
(Offered under: 2.96, 6.930)
(Subject meets with 2.961, 10.806J, 16.653J)
Prereq: --
Units: 3-1-8
Lecture: MW9.30-11 (3-270)

Introduction and overview of engineering management. Financial principles, management of innovation, technical strategy and best management practices. Case study method of instruction emphasizes participation in class discussion. Focus is on the development of individual skills and management tools. Registrants should be juniors or seniors. Students taking graduate subject 2.961 are expected to demonstrate higher level of proficiency in term projects.
A. V. d'Arbeloff, J.-H. Chun

6.931 Development of Inventions and Creative Ideas

()
Prereq: Permission of instructor
Units: 3-0-9
URL: http://www.mit.edu:8001/courses/6.901/patent.html

Role of the engineer as patent expert and as technical witness in court and patent interference and related proceedings. Rights and obligations of engineers in connection with educational institutions, government, and large and small businesses. Various manners of transplanting inventions into business operations, including development of New England and other US electronics and biotech industries and their different types of institutions. American systems of incentive to creativity apart from the patent laws in the atomic energy and space fields. Conducting periodic joint real-time class sessions and discussions by video-audio Internet conferencing, with other universities. For graduate students only; others see 6.901. Enrollment limited.
R. H. Rines

6.938 Engineering Risk-Benefit Analysis

()
Engineering School-Wide Elective Subject.
(Offered under: 1.155, 2.963, 3.577, 6.938, 10.816, 16.862, 22.82, ESD.72)
Prereq: 18.02
Units: 3-0-9
URL: http://web.mit.edu/6.938/www/

Emphasis on three methodologies pertaining to decision making in the presence of uncertainty: reliability and probabilistic risk assessment (RPRA), decision analysis (DA), and cost-benefit analysis (CBA). Risks of particular interest are those associated with large engineering projects such as the development of new products; the building, maintenance and operation of nuclear reactors and space systems. Presents and interprets some of the frameworks helpful for balancing risks and benefits in the situations that typically involve human safety, potential environmental effects, and large financial and technological uncertainties. Review of elementary probability theory and statistics included.
G. E. Apostolakis

6.945 Large-scale Symbolic Systems
(New)

()
Prereq: 6.001 and 6.034, or comparable programming experience
Units: 3-0-9

Concepts and techniques for the design and implementation of large software systems that can be adapted to uses not anticipated by the designer. Applications include compilers, computer-algebra systems, deductive systems, and some artificial intelligence applications. Means for decoupling goals from strategy. Mechanisms for implementing additive data-directed invocation. Work with partially-specified entities. Manage multiple viewpoints. Topics include combinators, generic operations, pattern matching, pattern-directed invocation, rule systems, backtracking, dependencies, indeterminacy, memoization, constraint propagation, and incremental refinement.
G. J. Sussman, C. P. Hanson

6.946J Classical Mechanics: A Computational Approach

()
(Same subject as 8.351J, 12.620J)
(Subject meets with 12.008)
Prereq: 8.01, 18.03, 6.001, or equivalent
Units: 3-3-6
URL: http://swissnet.ai.mit.edu/~gjs/6946/index.html

Classical mechanics in a computational framework. Lagrangian formulation. Action, variational principles. Hamilton's principle. Conserved quantities. Hamiltonian formulation. Surfaces of section. Chaos. Liouville's theorem and Poincar? integral invariants. Poincar?-Birkhoff and KAM theorems. Invariant curves. Cantori. Nonlinear resonances. Resonance overlap and transition to chaos. Properties of chaotic motion. Transport, diffusion, mixing. Symplectic integration. Adiabatic invariants. Many-dimensional systems, Arnold diffusion. Extensive use of computation to capture methods, for simulation, and for symbolic analysis.
J. Wisdom, G. J. Sussman

6.951 Graduate VI-A Internship

(, , )
Prereq: 6.921, 6.922, or 6.923
Units: 0-12-0 [P/D/F]
URL: http://dsg.harvard.edu/courses/hst951/
Consult instructor

Provides academic credit for a graduate assignment of graduate VI-A students at companies affiliated with the department's VI-A internship program. Enrollment limited to graduate students participating in the VI-A internship program.
M. Zahn

6.952 Graduate VI-A Internship

(, , )
Prereq: 6.951
Units: 0-12-0 [P/D/F]
Consult instructor

Provides academic credit for graduate students who require an additional term at the company to complete the graduate assignment of the department's VI-A internship program. This academic credit is for registration purposes only and cannot be used toward fulfilling the requirements of any degree program. Enrollment limited to graduate students participating in the VI-A internship program.
M. Zahn

6.961 Introduction to Research in Electrical Engineering and Computer Science

(, , )
Prereq: --
Units arranged
Compulsory: 1st Mtg Sept 5 At 5 Pm (34-401) Lecture: W5 (34-302)

Opportunity to become involved in graduate research, under guidance of a staff member, on a problem of mutual interest to student and supervisor. Recommended for all full-time graduate students entering the Department of Electrical Engineering and Computer Science. Individual programs subject to approval of professor in charge. Enrollment restricted to regular graduate students in Electrical Engineering and Computer Science. Normal registration is for 12 units.
T. P. Orlando

6.962?6.969 Special Studies in Electrical Engineering and Computer Science

(, , )
Prereq: --
Units arranged
6.962: TBA.
6.965: Consult department
6.966: Consult department
6.967: Consult department
6.968: Consult department

Opportunity for study of graduate-level topics related to electrical engineering and computer science but not included elsewhere in the curriculum. Registration under this subject normally used for situations involving individual study (under supervision of a faculty member) concerning topics of mutual interest to student and supervisor, but may, when appropriate, be used for small study groups. Normal registration is for 12 units. Registration subject to approval of professor in charge.
A. C. Smith

6.971?6.979 Special Subjects in Electrical Engineering and Computer Science

(, )
Prereq: Permission of instructor
Units arranged
URL: http://www.eecs.mit.edu/current/catalog/index.html
6.971: Lecture: MW11-12.30 (3-442) Lab: W2-5 (5-007) or F2-5 (5-007)
6.973: Lecture: MW11-12.30 (36-153) Recitation: F11 (38-301)
6.975: Lecture: M2-4 (34-304)
6.976: Lecture: F2-4 (4-145)
6.977: Lecture: R2.30-4 (34-303)
6.979: Consult department

Opportunity for group study of advanced subjects related to Electrical Engineering and Computer Science not otherwise included in curriculum. Offerings initiated by members of EECS faculty on an ad hoc basis, subject to departmental approval.
G. C. Verghese

6.980 Teaching Electrical Engineering and Computer Science

(, )
Prereq: --
Units arranged [P/D/F]
Consult department

For qualified students interested in gaining teaching experience. Classroom, tutorial, or laboratory teaching under the supervision of a faculty member. Enrollment limited by availability of suitable teaching assignments.
G. C. Verghese

6.981 Teaching Electrical Engineering and Computer Science

(, )
Prereq: --
Units arranged [P/D/F]
Consult department

For Teaching Assistants in Electrical Engineering and Computer Science, in cases where teaching assignment is approved for academic credit by the department.
G. C. Verghese

6.985-6.989 Special Subjects in Electrical Engineering

(, )
Prereq: Permission of instructor
Units arranged

Opportunity for group study of advanced subjects related to electrical engineering outside of the curriculum. Offerings initiated by members of the EECS faculty on an ad hoc basis, subject to department approval.
G. C. Verghese

6.991 Research in Electrical Engineering and Computer Science

(, , )
Prereq: --
Units arranged [P/D/F]
Consult department

For Research Assistants in Electrical Engineering and Computer Science, in cases where the assigned research is approved for academic credit by the department. Hours arranged with research supervisor.
A. C. Smith

6.CME Study at Cambridge University

(, )
Prereq: --
Units arranged

Provides credit for students studying at Cambridge University under the Cambridge-MIT Undergraduate Student Exchange Program. Credit may be used to satisfy specific SB degree requirements by arrangement with the department.
D. S. Boning, T. Akinwande

6.EPW UPOP IAP Workshop

()
Engineering School-Wide Elective Subject.
(Offered under: 1.EPW, 2.EPW, 3.EPW, 6.EPW, 10.EPW, 16.EPW, 22.EPW)
Prereq: --
Units: 3-0-0 [P/D/F]

Provides engineering sophomores the opportunity to build the core foundation of skills necessary to succeed in and prepare for a summer practice experience. Introduces concepts in product development, system dynamics, organizational dynamics, and effective communication. Also introduces concepts in ethics and character, and leadership and teamwork to ensure that students acquire an appreciation of the social, environmental, and ethical implications of organizational decision making. Subject is an interactive experience integrating lectures with role-playing, simulations, and group projects, where students apply these concepts in a case study context. Students are provided with a journal to be used during their summer training practice. Limited enrollment.
D. K. P. Yue

6.ThG Graduate Thesis

(, , )
Prereq: Permission of instructor
Units arranged
Consult department

Program of research leading to the writing of an SM, EE, ECS, PhD, or ScD thesis; to be arranged by the student and an appropriate MIT faculty member.
A. C. Smith

6.ThM Master of Engineering Program Thesis

(, , )
Prereq: 6.UAT
Units arranged
Consult department

Program of research leading to the writing of an MEng thesis; to be arranged by the student and an appropriate MIT faculty member. Restricted to MEng students who have been admitted to the MEng program.
A. C. Smith

6.UR Undergraduate Research in Electrical Engineering and Computer Science

(, , )
Prereq: --
Units arranged [P/D/F]
Consult department

Extended participation in the work of a faculty member or research group, including independent study of the literature, direct involvement in the group's research, and project work under an individual faculty member. Research is arranged by mutual agreement between the student and a member of the faculty of the Department of Electrical Engineering and Computer Science, and may continue over several terms. Forms and instructions for the initial letter of intent and final summary report are available in the department undergraduate office.
A. C. Smith