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 mit eecs courses data structures, testing and debugging, and algorithmic complexity.

Notes: Shows the degree requirements for Spring Each completed subject can only be used to satisfy at most one required subject. A subject is colored grey if not offered this academic year. 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.

Mit eecs courses

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. A — Z Calendar Archive Print. Degree Charts. Search Catalog Submit search. Overview Toggle Overview. Campus Life Toggle Campus Life. Academic Resources Toggle Academic Resources. Undergraduate Education Toggle Undergraduate Education. Academic Programs Toggle Academic Programs. Graduate Education Toggle Graduate Education.

Presents content taught in 6. Covers discrete geometry and algorithms underlying the reconfiguration of foldable structures, with applications to robotics, manufacturing, and biology.

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 contexts from electrical engineering and computer science. Our second goal is to show you that making mathematical models of real systems can help in the design and analysis of those systems. Finally, we have the more typical goals of teaching exciting and important basic material from electrical engineering and computer science, including modern software engineering, linear systems analysis, electronic circuits, and decision-making. This course has been designed for independent study.

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. The units for any subject that counts as one of the 17 GIR subjects cannot also be counted as units required beyond the GIRs. Chosen electives must satisfy each of the following categories: Advanced Departmental Laboratory, Independent Inquiry, and Probability. A subject may count toward more than one category. The PDF includes all information on this page and its related tabs. Subject course information includes any changes approved for the current academic year. A — Z Calendar Archive Print. Degree Charts. Search Catalog Submit search.

Mit eecs courses

The largest academic department at MIT, EECS offers a comprehensive range of degree programs, featuring expert faculty, state-of-the-art equipment and resources, and a hands-on educational philosophy that prioritizes playful, inventive experimentation. The interdisciplinary space between those three units creates fertile ground for technological innovation and discovery, and many of our students go on to start companies, conduct groundbreaking research, and teach the next generation of computer scientists, electrical engineers, computer scientists and engineers and AI engineers. Please go to the MIT Admissions website for all questions regarding undergraduate admissions.

The notebook imdb

Emphasizes expressing all hardware designs in a high-level hardware language and synthesizing the designs. Emphasizes the principles of biomolecular system design and diagnosis of designed systems. Restricted to juniors and seniors. Applications include biomolecular transport in tissues, electrophoresis, and microfluidics. Familiarity with elementary probability and real analysis is desirable. Conserved quantities, Hamiltonian formulation, surfaces of section, chaos, and Liouville's theorem. Moment generating and characteristic functions. Types, Values, Expressions; Variables and Binding. Topics include complexity classes, lower bounds, communication complexity, proofs and advice, and interactive proof systems in the quantum world; classical simulation of quantum circuits. Oscillations, damping, resonance. Students formulate their own device idea, either based on cantilevers or mixers, then implement and test their designs in the lab. Arrays as Lists of Lists.

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.

Model-based compensators; Q-parameterization; ill-posed optimization problems; dynamic augmentation; linear-quadratic optimization of controllers; H-infinity controller design; Mu-synthesis; model and compensator simplification; nonlinear effects. Treatment of electromechanical transducers, rotating and linear electric machines. Requires a research paper on a specific contemporary optical imaging topic. Departments Electrical Engineering and Computer Science. Utilizes research-based approaches through the application of multiple learning methods, including experiential role plays, case studies, assessments, feedback, and personal reflections. Emphasizes the relationship among technology, hardware organization, and programming systems in the evolution of computer architecture. Biology Toggle Biology. Dynamic programming as a unifying framework for sequential decision-making under uncertainty, Markov decision problems, and stochastic control. Topics Engineering. Covers a variety of topics in optimization, with a focus on non-convex optimization. Unfolding and folding three-dimensional polyhedra: edge unfolding, vertex unfolding, gluings, Alexandrov's Theorem, hinged dissections. Basic circuit building blocks.