| source MIT (X) |
level |
department Electrical Engineering and Computer Science (X) |
6.00 Introduction to Computer Science and Programming ( , ) Prereq: None Units: 3-7-2 Lecture: TR11 ( 34-101 ) Recitation: F11 ( 32-044 ) or F12 ( 32-044 ) or F1 ( 32-044 ) or F2 ( 32-044 ) +final Introduction to computer science and programming for students with little or no programming experience. Students learn how to program and how to use computational techniques to solve problems. Topics include algorithms, simulation techniques, and use of software libraries. Assignments are done using the Python programming language. J. V. Guttag
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6.01 Introduction to EECS I ( , ) Prereq: None. Coreq: Physics II (GIR) Units: 2-4-6 URL: http://mit.edu/6.01/index.html Lecture: T9.30-11 ( 32-123 ) Lab: T11.30-1,R10-1 ( 34-501 ) or T2-3.30,R2-5 ( 34-501 ) +final An integrated introduction to electrical engineering and computer science, taught using substantial laboratory experiments with mobile robots. Key issues in the design of engineered artifacts operating in the natural world: measuring and modeling system behaviors; assessing errors in sensors and effectors; specifying tasks; designing solutions based on analytical and computational models; planning, executing, and evaluating experimental tests of performance; refining models and designs. Issues addressed in the context of computer programs, control systems, probabilistic inference problems, circuits and transducers, which all play important roles in achieving robust operation of a large variety of engineered systems. 6 Engineering Design Points. H. Abelson, L. P. Kaelbling, J. K. White
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6.02 Introduction to EECS II ( , ) Prereq: 6.01 ; 18.03 or 18.06 Units: 3-3-6 Lecture: MW2 ( 32-123 ) Recitation: TR10 ( 36-112 ) or TR11 ( 36-112 ) or TR12 ( 36-112 ) or TR1 ( 36-112 ) or TR2 ( 26-168 ) or TR3 ( 26-168 ) +final An integrated introduction to electrical engineering and computer science, taught using substantial laboratory experiments that explore communication signals, systems and networks. Physical characterization and modeling of transmission systems in the time and frequency domains; analog and digital signaling; coding; detecting and correcting errors; relating information transmission rate to signal power, bandwidth and noise; engineering of packet-switched networks. These explorations are used to illustrate the role of abstraction and modularity in engineering design; building reliable systems using imperfect components; selecting appropriate design metrics; choosing effective representations for information; analyzing the performance and correctness of algorithms; and tradeoffs in complex systems. 6 Engineering Design Points. C. G. Sodini, C. J. Terman, H. Balakrishnan
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6.07J Projects in Microscale Engineering for the Life Sciences ( ) (Same subject as HST.410J ) Prereq: None Units: 2-4-3 A project-based introduction to manipulating and characterizing cells and biological molecules using microfabricated tools. In the first half of the term, students perform laboratory exercises designed to introduce the design, manufacture, and use of microfluidic channels; techniques for sorting and manipulating cells and biomolecules; and making quantitative measurements using optical detection and fluorescent labeling. In the second half of the term, students work in small groups to design and test a microfluidic device to solve a real-world problem of their choosing. Includes exercises in written and oral communication and team building. Enrollment limited to 20; preference to freshmen. D. Freeman, M. Gray, A. Aranyosi
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6.002 Circuits and Electronics ( , ) Prereq: 18.03 ; Physics II (GIR) or 6.01 Units: 4-1-7 Lecture: TR11 ( 32-123 ) Lab: TBA Recitation: WF11 ( 26-310 ) or WF12 ( 26-310 ) or WF1 ( 26-310 ) or WF2 ( 26-310 ) +final Fundamentals of the lumped circuit abstraction. Resistive elements and networks, independent and dependent sources, switches and MOS devices, digital abstraction, amplifiers, and energy storage elements. Dynamics of first- and second-order networks; design in the time and frequency domains; analog and digital circuits and applications. Design exercises. Occasional laboratory. 4 Engineering Design Points. A. Agarwal, J. del Alamo, J. H. Lang, D. J. Perreault
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6.003 Signals and Systems ( , ) Prereq: 6.002 or 6.02 Units: 5-0-7 URL: http://web.mit.edu/6.003 Lecture: TR12 ( 34-101 ) Lab: TBA Recitation: WF10 ( 34-301 ) or WF11 ( 34-301 ) or WF1 ( 34-301 ) or WF2 ( 34-301 ) +final Fundamentals of signal and system analysis, with applications drawn from filtering, audio and image processing, communications, and automatic control. Topics include convolution, Fourier series and transforms, sampling and discrete-time processing of continuous-time signals, modulation, Laplace and Z-transforms, and feedback systems. 4 Engineering Design Points. D. M. Freeman, Q. Hu, J. S. Lim
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6.004 Computation Structures ( , ) Prereq: 6.001 , 6.002 ; or 6.02 Units: 4-0-8 URL: http://6004.lcs.mit.edu/ Lecture: TR1 ( 32-123 ) Lab: TBA Recitation: WF10 ( 26-322 ) or WF11 ( 26-322 , 34-303 ) or WF12 ( 34-303 , 34-304 ) or WF1 ( 34-304 , 34-303 ) or WF2 ( 34-303 ) Introduces architecture of digital systems, emphasizing structural principles common to a wide range of technologies. Multilevel implementation strategies; definition of new primitives (e.g., gates, instructions, procedures, and processes) and their mechanization using lower-level elements. Analysis of potential concurrency; precedence constraints and performance measures; pipelined and multidimensional systems. Instruction set design issues; architectural support for contemporary software structures. 4 Engineering Design Points. S. A. Ward, C. J. Terman
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6.005 Elements of Software Construction ( , ) Prereq: 6.01 ; Coreq: 6.042J Units: 4-3-5 Lecture: TR2.30-4 ( 32-123 ) Lab: F1-4 ( 32-123 ) Recitation: W11 ( 24-121 ) or W12 ( 24-121 ) or W1 ( 24-121 ) or W2 ( 34-304 ) or W3 ( 34-304 ) Introduction to the fundamental principles and techniques of software development that have greatest impact on practice. Topics include capturing the essence of a problem by recognizing and inventing suitable abstractions; key paradigms, including state machines, functional programming, and object-oriented programming; use of design patterns to bridge gap between models and code; the role of interfaces and specification in achieving modularity and decoupling; reasoning about code using invariants; testing, test-case generation and coverage; essentials of programming with objects, functions, and abstract types. Includes exercises in modeling, design, implementation and reasoning. 12 Engineering Design Points. D. N. Jackson, R. C. Miller
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6.006 Introduction to Algorithms ( , ) Prereq: 6.01 , 6.042J Units: 4-0-8 Lecture: TR11 ( 4-370 ) Recitation: WF10 ( 34-302 ) or WF11 ( 34-302 ) or WF12 ( 34-302 ) or WF1 ( 34-302 ) or WF2 ( 34-302 ) or WF3 ( 34-302 ) +final Introduction to mathematical modeling of computational problems, as well as common algorithms, algorithmic paradigms, and data structures used to solve these problems. Emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems. R. L. Rivest, S. Devadas
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6.007 Electromagnetic Energy: From Motors to Lasers ( , ) Prereq: 6.01 , 18.03 Units: 4-1-7 Lecture: TWRF1 ( 37-212 ) Lab: T EVE (7-9 PM) or W EVE (7-9 PM) +final Applications of electromagnetic principles to classical and modern devices. Basic electrical components, electric motors and generators, power flow, and energy conversion in macroscopic to quantum-scale electrical and electromechanical systems. Photons and their interaction with matter in detectors, sources, optical fibers, and other devices and communication systems. V. Bulovic, R. J. Ram
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6.011 Introduction to Communication, Control, and Signal Processing ( ) Prereq: 6.003 ; 6.041 or 18.440 Units: 4-0-8 Signals, systems and inference as unifying themes in communication, control and signal processing. Input-output and state-space models of linear systems driven by deterministic and random signals; time- and transform-domain representations in discrete and continuous time. State feedback and observers. Probabilistic models; stochastic processes, correlation functions, power spectra, spectral factorization. Least-mean square error estimation; Wiener filtering. Detection; matched filters. A. V. Oppenheim, G. C. Verghese
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6.012 Microelectronic Devices and Circuits ( , ) Prereq: 6.002 Units: 4-0-8 Lecture: TR11 ( 4-237 ) Recitation: WF10 ( 34-304 ) or WF11 ( 34-304 ) or WF1 ( 36-372 ) +final Microelectronic device modeling, and basic microelectronic circuit analysis and design. Physical electronics of semiconductor junction and MOS devices. Relating terminal behavior to internal physical processes, developing circuit models, and understanding the uses and limitations of different models. Use of incremental and large-signal techniques to analyze and design transistor circuits, with examples chosen from digital circuits, linear amplifiers, and other integrated circuits. Design project. 4 Engineering Design Points. A. I. Akinwande, D. A. Antoniadis, C. G. Fonstad, Jr., C. G. Sodini
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6.013 Electromagnetics and Applications ( , ) (Subject meets with 6.630 ) Prereq: 6.003 or 6.007 Units: 4-0-8 Lecture: TR1 ( 32-141 ) Recitation: WF1 ( 26-302 ) or WF2 ( 26-302 ) Explores electromagnetic phenomena in modern applications, including wireless and optical communications, circuits, computer interconnects and peripherals, microwave communications and radar, antennas, sensors, micro-electromechanical systems, and power generation and transmission. Fundamentals include quasistatic and dynamic solutions to Maxwell's equations; waves, radiation, and diffraction; coupling to media and structures; guided waves; resonance; acoustic analogs; and forces, power, and energy. Students taking graduate version complete different assignments. Meets with 6.630 in Fall term only. D. H. Staelin, E. P. Ippen, M. Zahn
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6.021J Cellular Biophysics ( ) (Same subject as 2.791J , 20.370J ) (Subject meets with 2.794J , 6.521J , 20.470J , HST.541J ) Prereq: Physics II (GIR) ; 18.03 ; 2.005 , 6.002 , 6.003 , 6.071 , 10.301 , or permission of instructor Units: 5-2-5 Lecture: MWF10 ( 4-231 ) Lab: TBA Recitation: TR10 ( 34-302 ) or TR11 ( 34-302 ) +final Integrated overview of the biophysics of cells from prokaryotes to neurons, with a focus on mass transport and electrical signal generation across cell membrane. First half of course focuses on mass transport through membranes: diffusion, osmosis, chemically mediated, and active transport. Second half focuses on electrical properties of cells: ion transport to action potentials in electrically excitable cells. Electrical properties interpreted via kinetic and molecular properties of single voltage-gated ion channels. Laboratory and computer exercises illustrate the concepts. Provides instruction in written and oral communication. Students taking graduate version complete different assignments. Preference to juniors and seniors. 4 Engineering Design Points. D. M. Freeman, J. Han, J. Voldman
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6.022J Quantitative Systems Physiology ( ) (Same subject as 2.792J , 20.371J , HST.542J ) (Subject meets with 2.796J , 6.522J , 20.471J ) Prereq: Physics II (GIR) , 18.03 , or permission of instructor Units: 4-2-6 URL: http://web.mit.edu/6.022j/www/ Application of the principles of energy and mass flow to major human organ systems. Mechanisms of regulation and homeostasis. Anatomical, physiological and pathophysiological features of the cardiovascular, respiratory and renal systems. Systems, features and devices that are most illuminated by the methods of physical sciences. Laboratory work includes some animal studies. 2 Engineering Design Points. R. G. Mark, C. M. Stultz
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6.023J Fields, Forces and Flows in Biological Systems ( ) (Same subject as 2.793J , 20.330J ) Prereq: 2.005 , 6.021 , 20.320 or permission of instructor Units: 4-0-8 Introduction to electric fields, fluid flows, transport phenomena and their application to biological systems. Flux and continuity laws, Maxwell's equations, electro-quasistatics, electro-chemical-mechanical driving forces, conservation of mass and momentum, Navier-Stokes flows, and electrokinetics. Applications include biomolecular transport in tissues, electrophoresis, and microfluidics. J. Han, L. Griffith
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6.024J Molecular, Cellular, and Tissue Biomechanics ( ) (Same subject as 2.797J , 3.053J , 20.310J ) Prereq: 2.370 or 2.772J ; 18.03 or 3.016 ; Biology (GIR) Units: 4-0-8 Develops and applies scaling laws and the methods of continuum mechanics to biomechanical phenomena over a range of length scales. Topics include structure of tissues and the molecular basis for macroscopic properties; chemical and electrical effects on mechanical behavior; cell mechanics, motility and adhesion; biomembranes; biomolecular mechanics and molecular motors. Experimental methods for probing structures at the tissue, cellular, and molecular levels. R. D. Kamm
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6.033 Computer System Engineering ( ) Prereq: 6.004 Units: 5-0-7 URL: http://web.mit.edu/6.033/www/home.html Topics on the engineering of computer software and hardware systems: techniques for controlling complexity; strong modularity using client-server design, operating systems; performance, networks; naming; security and privacy; fault-tolerant systems, atomicity and coordination of concurrent activities, and recovery; 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. 4 Engineering Design Points. M. F. Kaashoek, H. Balakrishnan
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6.034 Artificial Intelligence ( , ) (Subject meets with HST.947 ) Prereq: 6.001 or 6.01 Units: 5-3-4 URL: http://courses.csail.mit.edu/6.034/ Lecture: MWF11 ( 34-101 ) Recitation: R1 ( 24-407 ) or R2 ( 24-407 ) or R3 ( 24-407 ) or F1 ( 26-210 ) or F2 ( 26-210 ) or F3 ( 26-210 ) +final Introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. Applications of rule chaining, heuristic search, constraint propagation, constrained search, inheritance, and other problem-solving paradigms. Applications of identification trees, neural nets, genetic algorithms, and other learning paradigms. Speculations on the contributions of human vision and language systems to human intelligence. Meets with HST.947 spring only. 4 Engineering Design Points. P. H. Winston
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6.035 Computer Language Engineering ( ) Prereq: 6.005 or 6.170 Units: 4-4-4 URL: http://web.mit.edu/6.035/6035.html Analyzes issues associated with the implementation of higher-level programming languages. Fundamental concepts, functions, and structures of compilers. The interaction of theory and practice. Using tools in building software. Includes a multi-person project on compiler design and implementation. 8 Engineering Design Points. S. P. Amarasinghe
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6.041 Probabilistic Systems Analysis ( , ) (Subject meets with 6.431 ) Prereq: Calculus II (GIR) Units: 4-0-8 Credit cannot also be received for 18.05 URL: http://web.mit.edu/6.041/www/home.html Lecture: MW12 ( 34-101 ) Recitation: TR10 ( 34-301 ) or TR11 ( 34-301 ) or TR12 ( 34-301 ) or TR1 ( 34-301 ) or TR2 ( 34-301 ) or TR3 ( 34-301 ) or TR10 ( 34-303 ) or TR11 ( 34-303 ) or TR2 ( 34-303 , 34-302 ) +final An introduction to probability theory, and the modeling and analysis of probabilistic systems. Sample space, probabilistic models, conditional probability. Discrete and continuous random variables. Transform techniques. Bernoulli and Poisson processes. Markov processes. Limit theorems and elements of statistical inference. Meets with graduate subject 6.431, but assignments differ. D. P. Bertsekas, J. N. Tsitsiklis
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6.042J Mathematics for Computer Science ( , ) (Same subject as 18.062J ) Prereq: Calculus I (GIR) Units: 5-0-7 URL: http://theory.csail.mit.edu/classes/6.042 Lecture: MWF11-12.30 ( 32-082 ) or MWF3-4.30 ( 32-082 ) +final Elementary discrete mathematics for computer science and engineering. Emphasis on mathematical definitions and proofs as well as on applicable methods. Topics: formal logic notation, proof methods; induction, well-ordering; sets, relations; elementary graph theory; integer congruences; asymptotic notation and growth of functions; permutations and combinations, counting principles; discrete probability. Further selected topics such as: recursive definition and structural induction; state machines and invariants; recurrences; generating functions. A. R. Meyer, T. Leighton
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6.045J Automata, Computability, and Complexity ( ) (Same subject as 18.400J ) Prereq: 6.042J Units: 4-0-8 URL: http://theory.lcs.mit.edu/classes/6.045/ Slower paced than 6.840J/18.404J. Introduces basic mathematical models of computation and the finite representation of infinite objects. Turing machines. Partial recursive functions. Church's Thesis. Undecidability. Reducibility and completeness. Time complexity and NP-completeness. Probabilistic computation. Interactive proof systems. S. Micali
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6.046J Design and Analysis of Algorithms ( ) (Same subject as 18.410J ) Prereq: 6.006 (alternatively: 6.001 ; 6.042 / 18.062 or 18.310 ) Units: 4-0-8 URL: http://theory.lcs.mit.edu/classes/6.046/ Lecture: TR11-12.30 ( 35-225 ) Recitation: F10 ( 36-144 ) or F11 ( 36-144 ) or F12 ( 2-146 ) or F1 ( 2-146 ) or F2 ( 2-146 ) or F3 ( 2-146 ) or F11 ( 36-156 ) or F12 ( 36-156 ) or F1 ( 36-156 ) or F2 ( 36-156 ) +final Techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics include sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; greedy algorithms; amortized analysis; graph algorithms; and shortest paths. Advanced topics may include network flow; computational geometry; number-theoretic algorithms; polynomial and matrix calculations; caching; and parallel computing. C. E. Leiserson, M. Goemans
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6.047 Computational Biology: Genomes, Networks, Evolution ( ) (Subject meets with 6.878J , HST.507J ) Prereq: 6.006 , 6.041 , and Biology (GIR) ; or permission of instructor Units: 3-0-9 Lecture: TR11-12.30 ( 2-105 ) Covers the algorithmic and machine learning foundations of computational biology, combining theory with practice. Principles of algorithm design, influential problems and techniques, and analysis of large-scale biological datasets. Topics include (a) genomes: sequence analysis, gene finding, RNA folding, genome alignment and assembly, database search; (b) networks: gene expression analysis, regulatory motifs, biological network analysis; (c) evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory. These are coupled with fundamental algorithmic techniques including: dynamic programming, hashing, Gibbs sampling, expectation maximization, hidden Markov models, stochastic context-free grammars, graph clustering, dimensionality reduction, Bayesian networks. M. Kellis
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