ELECTRICAL ENGINEERING AND COMPUTER SCIENCE (COURSE 6) ELECTRICAL ENGINEERING AND COMPUTER SCIENCE (COURSE 6) 6.002 Circuits and Electronics Prereq: Physics II (GIR); Coreq: 2.087 or 18.03 U (Fall, Spring) 3-2-7 units. REST Basic Undergraduate Subjects Fundamentals of linear systems and abstraction modeling through lumped electronic circuits. Linear networks involving independent 6.0001 Introduction to Computer Science Programming in and dependent sources, resistors, capacitors and inductors. Python Extensions to include nonlinear resistors, switches, transistors, Prereq: None operational ampliers and transducers. Dynamics of rst- and U (Fall, Spring; rst half of term) second-order networks; design in the time and frequency domains; 3-0-3 units signal and energy processing applications. Design exercises. Weekly Introduction to computer science and programming for students laboratory with microcontroller and transducers. with little or no programming experience. Students develop skills J. H. Lang, T. Palacios, D. J. Perreault, J. Voldman to program and use computational techniques to solve problems. Topics include the notion of computation, Python, simple algorithms 6.003 Signal Processing and data structures, testing and debugging, and algorithmic Prereq: 6.0001 and 18.03 complexity. Combination of 6.0001 and 6.0002 or 16.0002[J] counts U (Fall, Spring) as REST subject. Final given in the seventh week of the term. 6-0-6 units. REST A. Bell, J. V. Guttag Fundamentals of signal processing, focusing on the use of Fourier methods to analyze and process signals such as sounds and images. 6.0002 Introduction to Computational Thinking and Data Science Topics include Fourier series, Fourier transforms, the Discrete Fourier Prereq: 6.0001 or permission of instructor Transform, sampling, convolution, deconvolution, ltering, noise U (Fall, Spring; second half of term) reduction, and compression. Applications draw broadly from areas of 3-0-3 units contemporary interest with emphasis on both analysis and design. Credit cannot also be received for 16.0002[J], 18.0002[J] D. M. Freeman, A. Hartz Provides an introduction to using computation to understand real- world phenomena. Topics include plotting, stochastic programs, 6.004 Computation Structures probability and statistics, random walks, Monte Carlo simulations, Prereq: Physics II (GIR) and 6.0001 modeling data, optimization problems, and clustering. Combination U (Fall, Spring) of 6.0001 and 6.0002 counts as REST subject. 4-0-8 units. REST A. Bell, J. V. Guttag Provides an introduction to the design of digital systems and computer architecture. Emphasizes expressing all hardware designs 6.S00 Special Subject in Electrical Engineering and Computer in a high-level hardware language and synthesizing the designs. Science Topics include combinational and sequential circuits, instruction set Prereq: None abstraction for programmable hardware, single-cycle and pipelined U (Fall, Spring) processor implementations, multi-level memory hierarchies, virtual Not oered regularly; consult department memory, exceptions and I/O, and parallel systems. Units arranged S. Z. Hanono Wachman, D. Sanchez Covers subject matter not oered in the regular curriculum. Consult department to learn of oerings for a particular term. A. Bell, W. E. L. Grimson, J. V. Guttag Electrical Engineering and Computer Science (Course 6) | 3 ELECTRICAL ENGINEERING AND COMPUTER SCIENCE (COURSE 6) 6.006 Introduction to Algorithms 6.01 Introduction to EECS via Robotics Prereq: 6.042[J] and (6.0001 or Coreq: 6.009) Prereq: 6.0001 or permission of instructor U (Fall, Spring) U (Spring) 4-0-8 units 2-4-6 units. Institute LAB Introduction to mathematical modeling of computational problems, An integrated introduction to electrical engineering and computer as well as common algorithms, algorithmic paradigms, and science, taught using substantial laboratory experiments with data structures used to solve these problems. Emphasizes the mobile robots. Key issues in the design of engineered artifacts relationship between algorithms and programming, and introduces operating in the natural world: measuring and modeling system basic performance measures and analysis techniques for these behaviors; assessing errors in sensors and eectors; specifying problems. Enrollment may be limited. tasks; designing solutions based on analytical and computational E. Demaine, S. Devadas models; planning, executing, and evaluating experimental tests of performance; rening models and designs. Issues addressed in 6.008 Introduction to Inference the context of computer programs, control systems, probabilistic Prereq: Calculus II (GIR) or permission of instructor inference problems, circuits and transducers, which all play U (Fall) important roles in achieving robust operation of a large variety of 4-4-4 units. Institute LAB engineered systems. D. M. Freeman, A. Hartz, L. P. Kaelbling, T. Lozano-Perez Introduces probabilistic modeling for problems of inference and machine learning from data, emphasizing analytical 6.011 Signals, Systems and Inference and computational aspects. Distributions, marginalization, Prereq: 6.003 and (6.008, 6.041, or 18.600) conditioning, and structure, including graphical and neural U (Spring) network representations. Belief propagation, decision-making, 4-0-8 units classication, estimation, and prediction. Sampling methods and analysis. Introduces asymptotic analysis and information measures. Covers signals, systems and inference in communication, control Computational laboratory component explores the concepts and signal processing. Topics include input-output and state- introduced in class in the context of contemporary applications. space models of linear systems driven by deterministic and random Students design inference algorithms, investigate their behavior on signals; time- and transform-domain representations in discrete and real data, and discuss experimental results. continuous time; and group delay. State feedback and observers. P. Golland, G. W. Wornell Probabilistic models; stochastic processes, correlation functions, power spectra, spectral factorization. Least-mean square error 6.009 Fundamentals of Programming estimation; Wiener ltering. Hypothesis testing; detection; matched Prereq: 6.0001 lters. U (Fall, Spring) A. V. Oppenheim, G. C. Verghese 2-4-6 units. Institute LAB 6.012 Nanoelectronics and Computing Systems Introduces fundamental concepts of programming. Designed Prereq: 6.002 to develop skills in applying basic methods from programming U (Fall, Spring) languages to abstract problems. Topics include programming and 4-0-8 units Python basics, computational concepts, soware engineering, algorithmic techniques, data types, and recursion. Lab component Studies interaction between materials, semiconductor physics, consists of soware design, construction, and implementation of electronic devices, and computing systems. Develops intuition design. Enrollment may be limited. of how transistors operate. Topics range from introductory D. S. Boning, A. Chlipala, S. Devadas, A. Hartz semiconductor physics to modern state-of-the-art nano-scale devices. Considers how innovations in devices have driven historical progress in computing, and explores ideas for further improvements in devices and computing. Students apply material to understand how building improved computing systems requires knowledge of devices, and how making the correct device requires knowledge of computing systems. Includes a design project for practical application of concepts, and labs for experience building silicon transistors and devices. A. I. Akinwande, J. Kong, T. Palacios, M. Shulaker 4 | Electrical Engineering and Computer Science (Course 6) ELECTRICAL ENGINEERING AND COMPUTER SCIENCE (COURSE 6) 6.013 Electromagnetics Waves and Applications 6.02 Introduction to EECS via Communication Networks Prereq: Calculus II (GIR) and Physics II (GIR) Prereq: 6.0001 U (Spring) U (Fall) 3-5-4 units 4-4-4 units. Institute LAB Analysis and design of modern applications that employ Studies key concepts, systems, and algorithms to reliably electromagnetic phenomena for signals and power transmission communicate data in settings ranging from the cellular phone in RF, microwaves, optical and wireless communication systems. network and the Internet to deep space. Weekly laboratory Fundamentals include dynamic solutions for Maxwell's equations; experiments explore these areas in depth. Topics presented in three electromagnetic power and energy, waves in media, metallic and modules - bits, signals, and packets - spanning the multiple layers dielectric waveguides, radiation, and diraction; resonance; lters; of a communication system. Bits module includes information, and acoustic analogs. Lab activities range from building to testing entropy, data compression algorithms, and error correction with of devices and systems (e.g., antenna arrays, radars, dielectric block and convolutional codes. Signals module includes modeling waveguides). Students work in teams on self-proposed maker- physical channels and noise, signal design, ltering and detection, style design projects with a focus on fostering creativity, teamwork, modulation, and frequency-division multiplexing. Packets module and debugging skills. 6.002 and 6.003 are recommended but not includes switching and queuing principles, media access control, required. routing protocols, and data transport protocols. K. O'Brien, L. Daniel K. LaCurts 6.014 Electromagnetic Fields, Forces and Motion 6.021[J] Cellular Neurophysiology