Mit eecs courses

Department of Electrical Engineering and Computer Science.

EECS introduces students to major concepts in electrical engineering and computer science in an integrated and hands-on fashion. As students progress to increasingly advanced subjects, they gain considerable flexibility in shaping their own educational experiences. The majority of EECS majors begin with a choice of an introductory subject, exploring electrical engineering and computer science fundamentals by working on such concrete systems as robots, cell phone networks, medical devices, etc. Students gain understanding, competence, and maturity by advancing step-by-step through subjects of greater and greater complexity:. Throughout the undergraduate years, laboratory subjects, teamwork, independent projects, and research engage students with principles and techniques of analysis, design, and experimentation in a variety of EECS areas. The department also offers numerous programs that enable students to gain practical experience, ranging from collaborative industrial projects done on campus to term-long experiences at partner companies.

Mit eecs courses

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. Final given in the seventh week of the term. Prereq: 6. C20[J] , C20[J] , CSE. Provides an introduction to using computation to understand real-world phenomena. Topics include plotting, stochastic programs, probability and statistics, random walks, Monte Carlo simulations, modeling data, optimization problems, and clustering. Introduction to computer science and programming for students with no programming experience. Presents content taught in 6. Institute LAB. Introduces fundamental concepts of programming.

Enrollment limited; admittance may be controlled by lottery. Provides background and insight to understand current network literature and to perform research on networks with the aid of network design projects. Provides adequate foundation for MR physics to enable autumren porn of RF excitation design, efficient Fourier sampling, parallel encoding, reconstruction of non-uniformly sampled data, and the mit eecs courses of hardware imperfections on reconstruction performance.

Students must also take a 6-unit Common Ground disciplinary module to receive credit for this subject. Credit cannot be awarded without simultaneous completion of a 6-unit disciplinary module. Consult advisor. The PDF includes all information on this page and its related tabs. Subject course information includes any changes approved for the current academic year.

Covers applications of rule chaining, constraint propagation, constrained search, inheritance, statistical inference, and other problem-solving paradigms. Also addresses applications of identification trees, neural nets, genetic algorithms, support-vector machines, boosting, and other learning paradigms. Considers what separates human intelligence from that of other animals. Consult Department. Students taking graduate version complete additional assignments. Topics covered include: constraint satisfaction in discrete and continuous problems, logical representation and inference, Monte Carlo tree search, probabilistic graphical models and inference, planning in discrete and continuous deterministic and probabilistic models including MDPs and POMDPs. 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.

Mit eecs courses

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. Final given in the seventh week of the term. Prereq: 6. C20[J] , C20[J] , CSE. Provides an introduction to using computation to understand real-world phenomena. Topics include plotting, stochastic programs, probability and statistics, random walks, Monte Carlo simulations, modeling data, optimization problems, and clustering.

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Discusses how to identify if learning-based control can help solve a particular problem, how to formulate the problem in the learning framework, and what algorithm to use. Approximate dynamic programming for large-scale problems, and reinforcement learning. Topics include acoustic theory of speech production, acoustic-phonetics, signal representation, acoustic and language modeling, search, hidden Markov modeling, neural networks models, end-to-end deep learning models, and other machine learning techniques applied to speech and language processing topics. Optical waveguides, optical fibers and photonic devices for encoding and detection. Hands-on introduction to the design and construction of power electronic circuits and motor drives. Rate-distortion theory, vector quantizers. Same subject as 2. Parametric signal modeling, linear prediction, and lattice filters. Lectures cover attacks that compromise security as well as techniques for achieving security, based on recent research papers. EECS introduces students to major concepts in electrical engineering and computer science in an integrated and hands-on fashion.

Department of Electrical Engineering and Computer Science. Choose at least two subjects in the major that are designated as communication-intensive CI-M to fulfill the Communication Requirement.

Students cannot receive credit without simultaneous completion of a 6-unit disciplinary module. Laboratory exercises include activities such as the construction of oscillators for a simple musical instrument, a laser audio communicator, a countdown timer, an audio amplifier, and a feedback-controlled solid-state lighting system for daylight energy conservation. Mathematical models of psychophysical relations, incorporating quantitative knowledge of physiological transformations by the peripheral auditory system. Laboratory exercises shared with 6. Lectures cover signal processing topics relevant to the lab exercises, as well as background on the biological signals processed in the labs. Provides adequate foundation for MR physics to enable study of RF excitation design, efficient Fourier sampling, parallel encoding, reconstruction of non-uniformly sampled data, and the impact of hardware imperfections on reconstruction performance. Topics include protein-DNA interaction, chromatin accessibility, regulatory variant interpretation, medical image understanding, medical record understanding, therapeutic design, and experiment design the choice and interpretation of interventions. Instruction involves two lectures a week, practical experience through exercises, discussion of current research directions, and a group project. Interconnect models and parasitics, device sizing and logical effort, timing issues clock skew and jitter , and active clock distribution techniques. Geometric algorithms: convex hulls, linear programming in fixed or arbitrary dimension. Covers elements of probability theory, statistical estimation and inference, regression analysis, causal inference, and program evaluation.