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.

Detection and measurement of radio and optical signals encountered in communications, astronomy, remote sensing, instrumentation, and radar. Statistical analysis of signal processing systems, including radiometers, spectrometers, interferometers, and digital correlation systems. Matched filters and ambiguity functions. Communications channel performance. Measurement of random electromagnetic fields. Angular filtering properties of antennas, interferometers, and aperture synthesis systems. Radiative transfer and parameter estimation.

Most algorithms in computer vision and image analysis can be understood in terms of two important components: a representation and a modeling/estimation algorithm. The representation defines what information is important about the objects and is used to describe them. The modeling techniques extract the information from images to instantiate the representation for the particular objects present in the scene. In this seminar, we will discuss popular representations (such as contours, level sets, deformation fields) and useful methods that allow us to extract and manipulate image information, including manifold fitting, markov random fields, expectation maximization, clustering and others. For each concept -- a new representation or an estimation algorithm -- a lecture on the mathematical foundations of the concept will be followed by a discussion of two or three relevant research papers in computer vision, medical and biological imaging, that use the concept in different ways. We will aim to understand the fundamental techniques and to recognize situations in which these techniques promise to improve the quality of the analysis.

The 6.370 Robocraft programming competition is a unique challenge that combines battle strategy and software engineering. In short, the objective is to write the best player program for the computer game Robocraft. The course is offered during MIT's Independent Activities Period (IAP) - a special 4-week term that runs the full month of January.

This course will cover a number of advanced "selected topics" in the field of cryptography. The content may include, depending on the time available and student interest, topics such as: cryptographic protocols (general security definitions, composition theorems, protocols for specific tasks such as commitments and key exchange, general multi-party computation, composable notions of security for PK encryption and signatures), theory of extractors, privacy amplification, special-purpose factoring devices (and algorithms), concrete security arguments, differential cryptanalysis, public-key infrastructures, and protocols for electronic voting.

6.780 covers statistical modeling and the control of semiconductor fabrication processes and plants. Topics include design of experiments, response surface modeling, and process optimization; defect and parametric yield modeling; process/device/circuit yield optimization; monitoring, diagnosis, and feedback control of equipment and processes; analysis and scheduling of semiconductor manufacturing operations.

6.977 focuses on the physics of the interaction of photons with semiconductor materials. The band theory of solids is used to calculate the absorption and gain of semiconductor media. The rate equation formalism is used to develop the concepts of laser threshold, population inversion and modulation response. Matrix methods and coupled mode theory are applied to resonator structures such as distributed feedback lasers, tunable lasers and microring devices. The course is also intended to introduce students to noise models for semiconductor devices and to applications of optoelectronic devices to fiber optic communications.

Who invented the computer? Like many important technological developments, the invention of the computer cannot rightly be attributed to a single person. It is clear, however, that World War II was crucial to the emergence of the electronic digital computer. The first general-purpose electronic computer was the Electronic Numerical Integrator and Computer, the ENIAC, sponsored by the U.S. Army's Ballistics Research Laboratory at the Aberdeen Proving Ground in Maryland and developed at the the Moore School of Electrical Engineering at the University of Pennsylvania. The leaders of the project were physicist John W. Mauchly and a young electrical engineer, John Presper Eckert. In this interview, done in 1988 by David Allison and Peter Vogt for the Smithsonian Institution, Eckert described how the war provided "the opportunity"and the money to solve "engineering problems, scientific problems in general"that interested them.

This course presents software engineering concepts and principles in parallel with the software development life cycle. Topics addressed include the Software Development Life Cycle (SDLC), software modeling using Unified Modeling Language (UML), major phases of SDLC (Software Requirements and Analysis, Software Design, and Software Testing), and project management. Upon successful completion of this course, the student will be able to: demonstrate mastery of software engineering knowledge and skills, and professional issues necessary to practice software engineering; discuss principles of software engineering; describe software development life cycle models; learn principles of software modeling through UML as a modeling language; identify major activities and key deliverables in a software development life cycle during software requirements and analysis, software design, and software testing; apply the object-oriented methodology in software engineering to create UML artifacts for software analysis and requirements, software design, and software testing; apply project management concepts in a software engineering environment to manage project, people, and product; participate as an individual and as part of a team to deliver quality software systems. This free course may be completed online at any time. (Computer Science 302)

Computation over unreliable and anonymous protocols such as the World Wide Web. Problems of persistence, concurrency control, transactions, and transactions across multiple servers. The relational database management system as a tool for attacking these problems. Students work in small mentored teams on diverse projects. This is a course for students who already have some programming and software engineering experience. The goal is to give students some experience in dealing with those challenges that are unique to Internet applications, such as: concurrency; unpredictable load; security risks; opportunity for wide-area distributed computing; creating a reliable and stateful user experience on top of unreliable connections and stateless protocols; extreme requirements and absurd development schedules; requirements that change mid-way through a project, sometimes because of experience gained from testing with users; user demands for a multi-modal interface.

Survey of structural properties of natural languages, with special emphasis on the sound pattern. Representation of the lexicon. Physiology of speech production, articulatory phonetics. Acoustical theory of speech production; acoustical and articulatory descriptions of phonetic features and of prosodic aspects of speech. Perception of speech. Models of lexical access and of speech production and planning. Applications to recognition and generation of speech by machine, and to the study of speech disorders.

Fundamentals of detection and estimation for signal processing, communications, and control. Vector spaces of random variables. Bayesian and Neyman-Pearson hypothesis testing. Bayesian and nonrandom parameter estimation. Minimum-variance unbiased estimators and the Cramer-Rao bounds. Representations for stochastic processes; shaping and whitening filters; Karhunen-Loeve expansions. Detection and estimation from waveform observations. Advanced topics: linear prediction and spectral estimation; Wiener and Kalman filters.

Provides an integrated approach to understanding the practice of engineering in the real world. Students research the life cycle of a major engineering project, new technology, or startup company from multiple perspectives: technical, economic, political, cultural. Emphasis on analyzing engineering artifacts, understanding documentation, framing logical arguments, communicating effectively, and working in teams.

Students observe natural selection in action and investigate the underlying mechanism, including random mutation and differential fitness based on environmental characteristics. They do this through use of the free AVIDA-ED digital evolution software application.

Mathematical models of systems from observations of their behavior. Time series, state-space, and input-output models. Model structures, parametrization, and identifiability. Non-parametric methods. Prediction error methods for parameter estimation, convergence, consistency, andasymptotic distribution. Relations to maximum likelihood estimation. Recursive estimation; relation to Kalman filters; structure determination; order estimation; Akaike criterion; and bounded but unknown noise models. Robustness and practical issues.

The Teach Computing Curriculum is broken down into 4 key stages: Ages 5-7, Ages 7-11, Ages 11-14, and Ages 14-16.

Curriculum Information:
- Resources include lesson plans, slides, activity sheets, homework, and assessments
- Each key stage has a teacher guide and curriculum map to help you get started
- Built around an innovative progression framework where computing content has been organized into interconnected networks we call learning graphs
- Created by subject experts, using the latest pedagogical research and teacher feedback
All of the content is free for you to use, and in formats that make it easy for you to adapt it to meet the needs of your learners

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.

6.896ĺĘcovers mathematical foundations of parallel hardware, from computer arithmetic to physical design, focusing on algorithmic underpinnings. Topics covered include: arithmetic circuits, parallel prefix, systolic arrays, retiming, clocking methodologies, boolean logic, sorting networks, interconnection networks, hypercubic networks, P-completeness, VLSI layout theory, reconfigurable wiring, fat-trees, and area-time complexity.

6.895 covers theoretical foundations of general-purpose parallel computing systems, from languages to architecture. The focus is on the algorithmic underpinnings of parallel systems. The topics for the class will vary depending on student interest, but will likely include multithreading, synchronization, race detection, load balancing, memory consistency, routing networks, message-routing algorithms, and VLSI layout theory. The class will emphasize randomized algorithms and probabilistic analysis, including high-probability arguments.