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, mit eecs courses. Consult advisor.

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.

Mit eecs courses

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. It includes all of the materials you will need to understand the concepts covered in this subject. The materials in this course include:. Browse Course Material Syllabus. Instructor Insights. Shifting to a Practice-Theory-Practice Approach. Flipping the Classroom to Facilitate Active Learning. Online Tutoring Environment. Formative Assessment during Design Labs. Reflecting on Assessment.

Same subject as 7. Recommended prerequisite: 6. Electrical Engineering tab PDF.

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.

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.

Mit eecs courses

Starting fall , EECS will have one new undergraduate degree program Artificial Intelligence and Decision-Making and two revised degree programs and , in addition to the existing programs , , , , and The , , and degree programs from require an introductory subject one of 6. Since these subjects are not part of the new or revised degree programs, they may no longer be offered in the future. If you are in a degree, you can use any EECS elective subject as a substitution for the introductory-subject requirement. In the new and curricula, 6. The old version of 6. The new version of 6.

Philips norelco bodygroom series 7000

Continuation of 6. Co-Teaching the Course. Homework exercises are based on theoretical derivation and software implementation. Focuses on developing working software that solves real problems. Enrollment may be limited due to staffing and space requirements. The course reviews and introduces mathematical methods and techniques, which are fundamental in optics and photonics, but also useful in many other engineering specialties. These are coupled with fundamental algorithmic techniques including: dynamic programming, hashing, Gibbs sampling, expectation maximization, hidden Markov models, stochastic context-free grammars, graph clustering, dimensionality reduction, Bayesian networks. School of Science Toggle School of Science. Studies assembly language to facilitate a firm understanding of how high-level languages are translated to machine-level instructions. Consult Department Undergraduate Office. Studies assembly language to facilitate a firm understanding of how high-level languages are translated to machine-level instructions.

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.

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. Emphasis on Haskell and Ocaml, but no prior experience in these languages is assumed. Literature Toggle Literature. Provides a mathematical introduction to RL, including dynamic programming, statistical, and empirical perspectives, and special topics. An integrated introduction to electrical engineering and computer science, taught using substantial laboratory experiments with mobile robots. Familiarity with elementary probability and real analysis is desirable. The interaction of theory and practice. Equivalent to Computational issues and approximation techniques; Monte Carlo methods. Numerous application examples, such as motion control systems, power supplies, and radio-frequency power amplifiers. EM agorithm. Introduces the design and construction of power electronic circuits and motor drives. Students engage in extensive written and oral communication exercises, in the context of an approved advanced research project.