This course teaches simple reasoning techniques for complex phenomena: divide and conquer, dimensional analysis, extreme cases, continuity, scaling, successive approximation, balancing, cheap calculus, and symmetry. Applications are drawn from the physical and biological sciences, mathematics, and engineering...

This course explores the ultimate limits to communication and computation, with an emphasis on the physical nature of information and information processing. Topics include: information and computation, digital signals, codes and compression, applications such as biological representations of information...

This course is an introductory subject in the field of electric power systems and electrical to mechanical energy conversion. Electric power has become increasingly important as a way of transmitting and transforming energy in industrial, military and transportation uses. Electric power systems are also...

This course provides an integrated introduction to electrical engineering and computer science, taught using substantial laboratory experiments with mobile robots. Our primary goal is for you to learn to appreciate and use the fundamental design principles of modularity and abstraction in a variety of...

6.012 is the header course for the department's "Devices, Circuits and Systems" concentration. The topics covered include: modeling of microelectronic devices, basic microelectronic circuit analysis and design, physical electronics of semiconductor junction and metal-on-silicon (MOS) devices, relation...

This course analyzes issues associated with the implementation of higher-level programming languages. Topics covered include: fundamental concepts, functions, and structures of compilers, the interaction of theory and practice, and using tools in building software. The course includes a multi-person...

This course aims to give students the tools and training to recognize convex optimization problems that arise in scientific and engineering applications, presenting the basic theory, and concentrating on modeling aspects and results that are useful in applications. Topics include convex sets, convex...

This course discusses applications of electromagnetic and equivalent quantum mechanical principles to classical and modern devices. It covers energy conversion and power flow in both macroscopic and quantum-scale electrical and electromechanical systems, including electric motors and generators, electric...

This course introduces students to the fundamentals of nonlinear optimization theory and methods. Topics include unconstrained and constrained optimization, linear and quadratic programming, Lagrange and conic duality theory, interior-point algorithms and theory, Lagrangian relaxation, generalized programming...

This course introduces the principal algorithms for linear, network, discrete, nonlinear, dynamic optimization and optimal control. Emphasis is on methodology and the underlying mathematical structures. Topics include the simplex method, network flow methods, branch and bound and cutting plane methods...

Tired of solving Sudokus by hand? This class teaches you how to solve complex search problems with discrete optimization concepts and algorithms, including constraint programming, local search, and mixed-integer programming. Optimization technology is ubiquitous in our society. It schedules planes and...

A simple conceptual introduction to quantum mechanics and quantum computation. About this Course *Note - This is an Archived course*
This is a past/archived course. At this time, you can only explore this course in a self-paced fashion. Certain features of this course may not be active, but many people...

The course is a comprehensive introduction to the theory, algorithms and applications of integer optimization and is organized in four parts: formulations and relaxations, algebra and geometry of integer optimization, algorithms for integer optimization, and extensions of integer optimization.

Networks are ubiquitous in our modern society. The World Wide Web that links us to and enables information flows with the rest of the world is the most visible example. It is, however, only one of many networks within which we are situated. Our social life is organized around networks of friends and...

6.002 is designed to serve as a first course in an undergraduate electrical engineering (EE), or electrical engineering and computer science (EECS) curriculum. At MIT, 6.002 is in the core of department subjects required for all undergraduates in EECS. The course introduces the fundamentals of the lumped...

6.003 covers the fundamentals of signal and system analysis, focusing on representations of discrete-time and continuous-time signals (singularity functions, complex exponentials and geometrics, Fourier representations, Laplace and Z transforms, sampling) and representations of linear, time-invariant...

This course examines signals, systems and inference as unifying themes in communication, control and signal processing. Topics include input-output and state-space models of linear systems driven by deterministic and random signals; time- and transform-domain representations in discrete and continuous...

This course provides a challenging introduction to some of the central ideas of theoretical computer science. It attempts to present a vision of "computer science beyond computers": that is, CS as a set of mathematical tools for understanding complex systems such as universes and minds. Beginning in...

This course covers finite automata, context-free grammars, Turing machines, undecidable problems, and intractable problems (NP-completeness). I am pleased to be able to offer free over the Internet a course on Automata Theory, based on the material I have taught periodically at Stanford in the course...

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