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Course 6: Electrical Engineering and Computer Science 
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Programming & Software Engineering6.100A Introduction to Computer Science Programming in Python
(, ); first half of term
Prereq: None Units: 303 Credit cannot also be received for 6.100L URL: https://introcomp.mit.edu/spring24 Ends Mar 22. Lecture: MW34.30 (26100) Recitation: F10 (5134) or F11 (5134, 24121) or F12 (24121) or F1 (5134) or F2 (5134) or F1 (5217) or F2 (5217) Introduction to computer science and programming for students with little or no programming experience. Students develop skills to program and use computational techniques to solve problems. Topics include the notion of computation, Python, simple algorithms and data structures, testing and debugging, and algorithmic complexity. Combination of 6.100A and 6.100B or 16.C20 counts as REST subject. Final given in the seventh week of the term. Fall: A. Bell Spring: A. Bell Textbooks (Spring 2024) 6.100B Introduction to Computational Thinking and Data Science
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Prereq: 6.100A or permission of instructor Units: 303 Credit cannot also be received for 9.C20, 16.C20, 18.C20, CSE.C20 URL: https://introcomp.mit.edu/spring23 Begins Apr 1. Lecture: MW34.30 (26100) Recitation: F10 (5134) or F11 (5134, 24121) or F12 (24121) or F1 (5134) or F2 (5134) or F1 (5217) or F2 (5217) Provides an introduction to using computation to understand realworld phenomena. Topics include plotting, stochastic programs, probability and statistics, random walks, Monte Carlo simulations, modeling data, optimization problems, and clustering. Combination of 6.100A and 6.100B counts as REST subject. Fall: A. Bell Spring: A. Bell Textbooks (Spring 2024) 6.100L Introduction to Computer Science and Programming
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Prereq: None Units: 306 Credit cannot also be received for 6.100A URL: https://introcomp.mit.edu/6.100L_sp24 Lecture: MW34.30 (1190) Recitation: F10 (32123) Introduction to computer science and programming for students with no programming experience. Presents content taught in 6.100A over an entire semester. Students develop skills to program and use computational techniques to solve problems. Topics include the notion of computation, Python, simple algorithms and data structures, testing and debugging, and algorithmic complexity. Combination of 6.100L and 6.100B or 16.C20 counts as REST subject. Fall: A. Bell Spring: A. Bell Textbooks (Spring 2024) 6.1010 Fundamentals of Programming
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Prereq: 6.100A Units: 246 Lecture: MW9 (32124) or MW10 (32124, 32144) or MW11 (56154, 37212) or MW1 (1190, 4270) or MW2 (1190) or MW3 (3270) Lab: F101 (34501) or F25 (34501) +final Introduces fundamental concepts of programming. Designed to develop skills in applying basic methods from programming languages to abstract problems. Topics include programming and Python basics, computational concepts, software engineering, algorithmic techniques, data types, and recursion. Lab component consists of software design, construction, and implementation of design. Enrollment may be limited. Fall: A. Hartz Spring: A. Chlipala No required or recommended textbooks 6.1020 Software Construction
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Prereq: 6.1010 Units: 3012 Lecture: TR9.3011 (26100) +final Introduces fundamental principles and techniques of software development: how to write software that is safe from bugs, easy to understand, and ready for change. Topics include specifications and invariants; testing, testcase generation, and coverage; abstract data types and representation independence; design patterns for objectoriented programming; concurrent programming, including message passing and shared memory concurrency, and defending against races and deadlock; and functional programming with immutable data and higherorder functions. Includes weekly programming exercises and larger group programming projects. M. Goldman No required or recommended textbooks 6.1040 Software Design
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Prereq: 6.1020 and 6.1200 Units: 4014 Provides designfocused instruction on how to build complex software applications. Design topics include classic humancomputer interaction (HCI) design tactics (need finding, heuristic evaluation, prototyping, user testing), conceptual design (inventing, modeling and evaluating constituent concepts), social and ethical implications, abstract data modeling, and visual design. Implementation topics include reactive frontends, web services, and databases. Students work both on individual projects and a larger team project in which they design and build fullstack web applications. D. Jackson 6.1060 Software Performance Engineering
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Prereq: 6.1020, 6.1210, and 6.1910 Units: 3123 Projectbased introduction to building efficient, highperformance and scalable software systems. Topics include performance analysis, algorithmic techniques for high performance, instructionlevel optimizations, vectorization, cache and memory hierarchy optimization, and parallel programming. C. Leiserson 6.5060 Algorithm Engineering
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Prereq: 6.1060 and 6.1220 Units: 309 Lecture: TR1112.30 (3370) Covers the theory and practice of algorithms and data structures. Topics include models of computation, algorithm design and analysis, and performance engineering of algorithm implementations. Presents the design and implementation of sequential, parallel, cacheefficient, and externalmemory algorithms. Illustrates many of the principles of algorithm engineering in the context of parallel algorithms and graph problems. J. Shun No textbook information available 6.5080 Multicore Programming
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(Subject meets with 6.5081) Prereq: 6.1210 Units: 408 Lecture: W112 (45102) Recitation: F11 (66144) or R11 (34302) or F4 (34302) Introduces principles and core techniques for programming multicore machines. Topics include locking, scalability, concurrent data structures, multiprocessor scheduling, load balancing, and stateoftheart synchronization techniques, such as transactional memory. Includes sequence of programming assignments on a large multicore machine, culminating with the design of a highly concurrent application. Students taking graduate version complete additional assignments. N. Shavit No textbook information available 6.5081 Multicore Programming
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(Subject meets with 6.5080) Prereq: 6.1210 Units: 408 Lecture: W112 (45102) Recitation: F11 (66144) or R11 (34302) or F4 (34302) Introduces principles and core techniques for programming multicore machines. Topics include locking, scalability, concurrent data structures, multiprocessor scheduling, load balancing, and stateoftheart synchronization techniques, such as transactional memory. Includes sequence of programming assignments on a large multicore machine, culminating with the design of a highly concurrent application. Students taking graduate version complete additional assignments. N. Shavit No textbook information available Programming Languages6.1100 Computer Language Engineering
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Prereq: 6.1020 and 6.1910 Units: 444 Lecture: MWF11 (32124) Recitation: TR12 (32124) Analyzes issues associated with the implementation of higherlevel programming languages. Fundamental concepts, functions, and structures of compilers. The interaction of theory and practice. Using tools in building software. Includes a multiperson project on compiler design and implementation. M. Rinard No textbook information available 6.1120 Dynamic Computer Language Engineering
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Prereq: 6.1020 or 6.1910 Units: 444 Studies the design and implementation of modern, dynamic programming languages. Topics include fundamental approaches for parsing, semantics and interpretation, virtual machines, garbage collection, justintime machine code generation, and optimization. Includes a semesterlong, group project that delivers a virtual machine that spans all of these topics. M. Rinard 6.5110 Foundations of Program Analysis
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Prereq: 6.1100 Units: 309 Presents major principles and techniques for program analysis. Includes formal semantics, type systems and typebased program analysis, abstract interpretation and model checking and synthesis. Emphasis on Haskell and Ocaml, but no prior experience in these languages is assumed. Student assignments include implementing of techniques covered in class, including building simple verifiers. A. SolarLezama 6.5120 Formal Reasoning About Programs
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Prereq: 6.1020 and 6.1200 Units: 309 Surveys techniques for rigorous mathematical reasoning about correctness of software, emphasizing commonalities across approaches. Introduces interactive computer theorem proving with the Coq proof assistant, which is used for all assignments, providing immediate feedback on soundness of logical arguments. Covers common programproof techniques, including operational semantics, model checking, abstract interpretation, type systems, program logics, and their applications to functional, imperative, and concurrent programs. Develops a common conceptual framework based on invariants, abstraction, and modularity applied to state and labeled transition systems. Staff 6.5150 Largescale Symbolic Systems
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(Subject meets with 6.5151) Prereq: 6.4100 or permission of instructor Units: 309 Lecture: MWF2 (35225) 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, computeralgebra systems, deductive systems, and some artificial intelligence applications. Covers means for decoupling goals from strategy, mechanisms for implementing additive datadirected invocation, work with partiallyspecified entities, and how to manage multiple viewpoints. Topics include combinators, generic operations, pattern matching, patterndirected invocation, rule systems, backtracking, dependencies, indeterminacy, memoization, constraint propagation, and incremental refinement. Students taking graduate version complete additional assignments. G. J. Sussman Textbooks (Spring 2024) 6.5151 Largescale Symbolic Systems
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(Subject meets with 6.5150) Prereq: 6.4100 or permission of instructor Units: 309 Lecture: MWF2 (35225) 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, computeralgebra systems, deductive systems, and some artificial intelligence applications. Covers means for decoupling goals from strategy, mechanisms for implementing additive datadirected invocation, work with partiallyspecified entities, and how to manage multiple viewpoints. Topics include combinators, generic operations, pattern matching, patterndirected invocation, rule systems, backtracking, dependencies, indeterminacy, memoization, constraint propagation, and incremental refinement. Students taking graduate version complete additional assignments. G. Sussman Textbooks (Spring 2024) 6.5160[J] Classical Mechanics: A Computational Approach
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(Same subject as 8.351[J], 12.620[J]) Prereq: Physics I (GIR), 18.03, and permission of instructor Units: 336 Classical mechanics in a computational framework, Lagrangian formulation, action, variational principles, and Hamilton's principle. Conserved quantities, Hamiltonian formulation, surfaces of section, chaos, and Liouville's theorem. Poincaré integral invariants, PoincaréBirkhoff and KAM theorems. Invariant curves and cantori. Nonlinear resonances, resonance overlap and transition to chaos. Symplectic integration. Adiabatic invariants. Applications to simple physical systems and solar system dynamics. Extensive use of computation to capture methods, for simulation, and for symbolic analysis. Programming experience required. J. Wisdom, G. J. Sussman Theoretical Computer Science6.1200[J] Mathematics for Computer Science
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(Same subject as 18.062[J]) Prereq: Calculus I (GIR) Units: 507 Lecture: TR2.304 (26100) Recitation: WF10 (38166, 36155) or WF11 (38166, 36155, 26168) or WF12 (38166, 36156, 26168) or WF1 (38166, 36156, 35310) or WF2 (38166, 36156, 35308) or WF3 (38166, 36156) +final Elementary discrete mathematics for science and engineering, with a focus on mathematical tools and proof techniques useful in computer science. Topics include logical notation, sets, relations, elementary graph theory, state machines and invariants, induction and proofs by contradiction, recurrences, asymptotic notation, elementary analysis of algorithms, elementary number theory and cryptography, permutations and combinations, counting tools, and discrete probability. Fall: Z. Abel Spring: Z. Abel No required or recommended textbooks 6.120A Discrete Mathematics and Proof for Computer Science
(); second half of term
Prereq: Calculus I (GIR) Units: 303 Lecture: TR12.30 (32144) Subset of elementary discrete mathematics for science and engineering useful in computer science. Topics may include logical notation, sets, done relations, elementary graph theory, state machines and invariants, induction and proofs by contradiction, recurrences, asymptotic notation, elementary analysis of algorithms, elementary number theory and cryptography, permutations and combinations, counting tools. M. Medard No textbook information available 6.1210 Introduction to Algorithms
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Prereq: 6.100A and (6.120A or 6.1200) Units: 507 Lecture: TR1112.30 (26100) Recitation: WF10 (34304, 34303, 36156) or WF11 (34304, 34303, 34301) or WF12 (34304, 34301) or WF1 (34304, 34303, 34301) or WF2 (34304, 34303, 34301) or WF3 (34304, 34301) or WF4 (34304) or WF10 (36153) or WF4 (36155) +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. Enrollment may be limited. Fall: M. Karchmer Spring: B. Chapman Textbooks (Spring 2024) 6.1220[J] Design and Analysis of Algorithms
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(Same subject as 18.410[J]) Prereq: 6.1200 and 6.1210 Units: 408 Lecture: TR1112.30 (32123) Recitation: F9 (36112) or F10 (36112) or F11 (36112) or F12 (36112) or F1 (36112) or F2 (36112) or F3 (36112) or F10 (24121) or F11 (4265) or F12 (4265) or F1 (4265) or F2 (24121) or F3 (24121) +final Techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics include sorting; search trees, heaps, and hashing; divideandconquer; dynamic programming; greedy algorithms; amortized analysis; graph algorithms; and shortest paths. Advanced topics may include network flow; computational geometry; numbertheoretic algorithms; polynomial and matrix calculations; caching; and parallel computing. Fall: S. Raghuraman Spring: S. Raghuraman Textbooks (Spring 2024) 6.1400[J] Computability and Complexity Theory
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(Same subject as 18.400[J]) Prereq: (6.1200 and 6.1210) or permission of instructor Units: 408 Lecture: TR2.304 (34304) Recitation: F11 (4257) or F1 (24121) Mathematical introduction to the theory of computing. Rigorously explores what kinds of tasks can be efficiently solved with computers by way of finite automata, circuits, Turing machines, and communication complexity, introducing students to some major open problems in mathematics. Builds skills in classifying computational tasks in terms of their difficulty. Discusses other fundamental issues in computing, including the Halting Problem, the ChurchTuring Thesis, the P versus NP problem, and the power of randomness. D. Minzer No textbook information available 6.1420 Fixed Parameter and Finegrained Computation
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Prereq: 6.1200, 6.1210, and (6.1220, 6.1400, or 18.404) Units: 309 An overview of the theory of parameterized algorithms and the "problemcentric" theory of finegrained complexity, both of which reconsider how to measure the difficulty and feasibility of solving computational problems. Topics include: fixedparameter tractability (FPT) and its characterizations, the Whierarchy (W[1], W[2], W[P], etc.), 3sumhardness, allpairs shortest paths (APSP)equivalences, strong exponential time hypothesis (SETH) hardness of problems, and the connections to circuit complexity and other aspects of computing. Staff 6.5210[J] Advanced Algorithms
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(Same subject as 18.415[J]) Prereq: 6.1220 and (6.1200, 6.3700, or 18.600) Units: 507 Firstyear 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. Staff 6.5220[J] Randomized Algorithms
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(Same subject as 18.416[J]) Prereq: (6.1200 or 6.3700) and (6.1220 or 6.5210) Units: 507 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. D. R. Karger 6.5230 Advanced Data Structures
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Prereq: 6.1220 Units: 309 Subject Cancelled 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. Selfadjusting data structures; linear search; splay trees; dynamic optimality. Integer data structures; word RAM. Predecessor problem; van Emde Boas priority queues; yfast trees; fusion trees. Lower bounds; cellprobe model; round elimination. Dynamic graphs; linkcut trees; dynamic connectivity. Strings; text indexing; suffix arrays; suffix trees. Static data structures; compact arrays; rank and select. Succinct data structures; tree encodings; implicit data structures. Externalmemory and cacheoblivious data structures; Btrees; buffer trees; tree layout; orderedfile maintenance. Temporal data structures; persistence; retroactivity. Staff 6.5240 Sublinear Time Algorithms
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Prereq: 6.1220 or permission of instructor Units: 309 Sublinear time algorithms understand parameters and properties of input data after viewing only a minuscule fraction of it. Tools from number theory, combinatorics, linear algebra, optimization theory, distributed algorithms, statistics, and probability are covered. Topics include: testing and estimating properties of distributions, functions, graphs, strings, point sets, and various combinatorial objects. Staff 6.5250[J] Distributed Algorithms
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(Same subject as 18.437[J]) Prereq: 6.1220 Units: 309 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. M. Ghaffari, N. A. Lynch 6.5310 Geometric Folding Algorithms: Linkages, Origami, Polyhedra
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Prereq: 6.1220 or permission of instructor Units: 309 Covers discrete geometry and algorithms underlying the reconfiguration of foldable structures, with applications to robotics, manufacturing, and biology. Linkages made from onedimensional rods connected by hinges: constructing polynomial curves, characterizing rigidity, characterizing unfoldable versus locked, protein folding. Folding twodimensional paper (origami): characterizing flat foldability, algorithmic origami design, onecut magic trick. Unfolding and folding threedimensional polyhedra: edge unfolding, vertex unfolding, gluings, Alexandrov's Theorem, hinged dissections. E. D. Demaine 6.5320 Geometric Computing
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Prereq: 6.1220 Units: 309 Lecture: TR12.30 (32124) Introduction to the design and analysis of algorithms for geometric problems, in low and highdimensional 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. Highdimensional nearest neighbor search and lowdistortion embeddings between metric spaces. Geometric algorithms for massive data sets: external memory and streaming algorithms. Geometric optimization. P. Indyk No textbook information available 6.5340 Topics in Algorithmic Game Theory
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Prereq: 6.1210 or 6.1220 Units: 309 Subject Cancelled Presents research topics at the interface of computer science and game theory, with an emphasis on algorithms and computational complexity. Explores the types of gametheoretic tools that are applicable to computer systems, the loss in system performance due to the conflicts of interest of users and administrators, and the design of systems whose performance is robust with respect to conflicts of interest inside the system. Algorithmic focus is on algorithms for equilibria, the complexity of equilibria and fixed points, algorithmic tools in mechanism design, learning in games, and the price of anarchy. Staff 6.5350 Matrix Multiplication and Graph Algorithms
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Prereq: 6.1220 Units: 309 Explores topics around matrix multiplication (MM) and its use in the design of graph algorithms. Focuses on problems such as transitive closure, shortest paths, graph matching, and other classical graph problems. Explores fast approximation algorithms when MM techniques are too expensive. Staff 6.5400[J] Theory of Computation
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(Same subject as 18.4041[J]) (Subject meets with 18.404) Prereq: 6.1200 or 18.200 Units: 408 A more extensive and theoretical treatment of the material in 6.1400J/18.400J, emphasizing computability and computational complexity theory. Regular and contextfree 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. Students in Course 18 must register for the undergraduate version, 18.404. M. Sipser 6.5410[J] Advanced Complexity Theory
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(Same subject as 18.405[J]) Prereq: 18.404 Units: 309 Lecture: TR2.304 (35225) Current research topics in computational complexity theory. Nondeterministic, alternating, probabilistic, and parallel computation models. Boolean circuits. Complexity classes and complete sets. The polynomialtime hierarchy. Interactive proof systems. Relativization. Definitions of randomness. Pseudorandomness and derandomizations. Interactive proof systems and probabilistically checkable proofs. R. Williams Textbooks (Spring 2024) 6.5420 Randomness and Computation
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Prereq: 6.1220 and 18.4041 Units: 309 The power and sources of randomness in computation. Connections and applications to computational complexity, computational learning theory, cryptography and combinatorics. Topics include: probabilistic proofs, uniform generation and approximate counting, Fourier analysis of Boolean functions, computational learning theory, expander graphs, pseudorandom generators, derandomization. R. Rubinfeld 6.5430 Quantum Complexity Theory
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Prereq: 6.1400, 18.4041, and 18.435 Units: 309 Subject Cancelled Introduction to quantum computational complexity theory, the study of the fundamental capabilities and limitations of quantum computers. Topics include complexity classes, lower bounds, communication complexity, proofs and advice, and interactive proof systems in the quantum world; classical simulation of quantum circuits. The objective is to bring students to the research frontier. Staff Security & Cryptography6.1600 Foundations of Computer Security
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Prereq: 6.1210 and 6.1800 Units: 408 Fundamental notions and big ideas for achieving security in computer systems. Topics include cryptographic foundations (pseudorandomness, collisionresistant hash functions, authentication codes, signatures, authenticated encryption, publickey encryption), systems ideas (isolation, noninterference, authentication, access control, delegation, trust), and implementation techniques (privilege separation, fuzzing, symbolic execution, runtime defenses, sidechannel attacks). Case studies of how these ideas are realized in deployed systems. Lab assignments apply ideas from lectures to learn how to build secure systems and how they can be attacked. H. CorriganGibbs 6.5610 Applied Cryptography and Security
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Prereq: 6.1200 and 6.1800 Units: 408 Lecture: MW1112.30 (6120) Recitation: F11 (1190) Emphasis on applied cryptography. May include: basic notion of systems security, cryptographic hash functions, symmetric cryptography (onetime pad, block ciphers, stream ciphers, message authentication codes), hash functions, keyexchange, publickey cryptography (encryption, digital signatures), elliptic curve cryptography, secretsharing, fully homomorphic encryption, zeroknowledge proofs, and electronic voting. Assignments include a final group project. Topics may vary year to year. H. CorriganGibbs No textbook information available 6.5620[J] Cryptography and Cryptanalysis
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(Same subject as 18.425[J]) Prereq: 6.1220 Units: 309 A rigorous introduction to modern cryptography. Emphasis on the fundamental cryptographic primitives of publickey encryption, digital signatures, pseudorandom number generation, and basic protocols and their computational complexity requirements. V. Vaikuntanathan 6.5630 Advanced Topics in Cryptography
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Prereq: 6.5620 Units: 309 Indepth exploration of recent results in cryptography. Y. Kalai 6.5660 Computer Systems Security
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Prereq: 6.1020 and 6.1800 Units: 363 Lecture: TR2.304 (45230) +final Design and implementation of secure computer systems. Lectures cover attacks that compromise security as well as techniques for achieving security, based on recent research papers. Topics include operating system security, privilege separation, capabilities, languagebased security, cryptographic network protocols, trusted hardware, and security in web applications and mobile phones. Labs involve implementing and compromising a web application that sandboxes arbitrary code, and a group final project. N. Zeldovich No textbook information available Computer Systems6.1800 Computer Systems Engineering
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Prereq: 6.1910 Units: 516 URL: https://mit.edu/6.1800 Lecture: MW2 (26100) Lab: F1 (36144) or F2 (36144) or F1 (26142) or F2 (26142) or F1 (26210) or F2 (26210) or F1 (26314) or F2 (26314) or F1 (4149) or F2 (4149) or F1 (4261) or F2 (4261) or F1 (4145) or F2 (4145) or F1 (24307) or F2 (24307) Recitation: TR10 (34301, 36153, 36156) or TR11 (34301, 36153, 36156, 34303) or TR12 (34303, 36155) or TR1 (34302, 35310, 36155, 24307) or TR2 (34302, 35308, 24307) +final Topics on the engineering of computer software and hardware systems: techniques for controlling complexity; strong modularity using clientserver design, operating systems; performance, networks; naming; security and privacy; faulttolerant 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. Includes a single, semesterlong design project. Students engage in extensive written communication exercises. Enrollment may be limited. K. LaCurts Textbooks (Spring 2024) 6.1810 Operating System Engineering
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Prereq: 6.1910 Units: 309 Design and implementation of operating systems, and their use as a foundation for systems programming. Topics include virtual memory, file systems, threads, context switches, kernels, interrupts, system calls, interprocess communication, coordination, and interaction between software and hardware. A multiprocessor operating system for RISCV, xv6, is used to illustrate these topics. Individual laboratory assignments involve extending the xv6 operating system, for example to support sophisticated virtual memory features and networking. R. Morris 6.1820[J] Mobile and Sensor Computing
()Not offered regularly; consult department (Same subject as MAS.453[J]) Prereq: 6.1800 or permission of instructor Units: 309 Focuses on "Internet of Things" (IoT) systems and technologies, sensing, computing, and communication. Explores fundamental design and implementation issues in the engineering of mobile and sensor computing systems. Topics include batteryfree sensors, seeing through wall, robotic sensors, vital sign sensors (breathing, heartbeats, emotions), sensing in cars and autonomous vehicles, subsea IoT, sensor security, positioning technologies (including GPS and indoor WiFi), inertial sensing (accelerometers, gyroscopes, inertial measurement units, deadreckoning), embedded and distributed system architectures, sensing with radio signals, sensing with microphones and cameras, wireless sensor networks, embedded and distributed system architectures, mobile libraries and APIs to sensors, and application case studies. Includes readings from research literature, as well as laboratory assignments and a significant term project. Staff 6.1850 Computer Systems and Society
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Prereq: 6.1800 Units: 309 Explores the impact of computer systems on individual humans, society, and the environment. Examines large and smallscale power structures that stem from lowlevel technical design decisions, the consequences of those structures on society, and how they can limit or provide access to certain technologies. Students learn to assess design decisions within an ethical framework and consider the impact of their decisions on nonusers. Case studies of working systems and readings from the current literature provide comparisons and contrasts. Possible topics include the implications of hierarchical designs (e.g., DNS) for scale; how layered models influence what parts of a network have the power to take certain actions; and the environmental impact of proofofworkbased systems such as Bitcoin. Enrollment may be limited. K. Lacurts 6.5810 Operating System Engineering
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Prereq: 6.1020 and 6.1800 Units: 363 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 x86based personal computer) that implements the basic operating system abstractions and a shell. Knowledge of programming in the C language is a prerequisite. M. Kaashoek 6.5820 Computer Networks
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Prereq: 6.1800 or permission of instructor Units: 408 Topics on the engineering and analysis of network protocols and architecture, including architectural principles for designing heterogeneous networks; transport protocols; Internet routing; router design; congestion control and network resource management; wireless networks; network security; naming; overlay and peertopeer networks. Readings from original research papers. Semesterlong project and paper. M. Ghobadi 6.5830 Database Systems
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(Subject meets with 6.5831) Prereq: (6.1800 and (6.1210 or 6.1220)) or permission of instructor Units: 309 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; pubsub systems; semi structured data and XML querying. Lecture and readings from original research papers. Semesterlong project and paper. Students taking graduate version complete different assignments. Enrollment may be limited. S. R. Madden 6.5831 Database Systems
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(Subject meets with 6.5830) Prereq: (6.1800 and (6.1210 or 6.1220)) or permission of instructor Units: 309 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; pubsub systems; semi structured data and XML querying. Lecture and readings from original research papers. Semesterlong project and paper. Students taking graduate version complete different assignments. Enrollment may be limited. S. Madden 6.5840 Distributed Computer Systems Engineering
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Prereq: 6.1800 and permission of instructor Units: 309 Lecture: TR12.30 (E25111) +final Abstractions and implementation techniques for engineering distributed systems: remote procedure call, threads and locking, client/server, peertopeer, consistency, fault tolerance, and security. Readings from current literature. Individual laboratory assignments culminate in the construction of a faulttolerant and scalable network file system. Programming experience with C/C++ required. Enrollment limited. R. Morris No textbook information available 6.5850 Principles of Computer Systems
()Not offered regularly; consult department Prereq: Permission of instructor Units: 309 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. M. F. Kaashoek, B. Lampson, N. B. Zeldovich Computer Architecture6.1903 Introduction to Lowlevel Programming in C and Assembly
(); first half of term

  6.10/6.50    6.20/6.60    6.30/6.70    6.40/6.80    6.90/6.ZZ   