Mit computer science & artificial intelligence laboratory

Computing majors are the largest at MIT. A joint venture between the Schwarzman College of Computing and the School of Engineering, the Department of Electrical Engineering and Computer Science (EECS) offers several undergraduate degree programs which satisfy a variety of interests.

Led by world-class faculty, EECS students engage in a rigorous, hands-on curriculum that prepares them for a wide range of careers in the public and private sectors. Many students also go on to pursue graduate studies and careers in academia.

Interested in pursuing an undergraduate degree in computing at MIT? Undergraduates begin their studies here without a declared major (aka, Course). All prospective students should direct their questions and apply through MIT Admissions.

Majors

• Electrical Science and Engineering. Course 6-1 studies circuits and devices, materials and nanotechnology, communications, control and signal processing, and applied physics.
• Electrical Engineering and Computer Science. Course 6-2 combines the department’s key focal areas into a flexible major that prepares students for careers and research fields where an understanding of both hardware and software systems is essential.
• Computer Science and Engineering. Course 6-3 centers on computation structures, artificial intelligence, software engineering, computer algorithms, and computer systems.
• Artificial Intelligence and Decision Making. New in Fall 2022, Course 6-4 focuses on the analysis and synthesis of systems that interact with an external world via perception, communication, and action, and that learn, make decisions and adapt in a changing environment.

Blended majors

• Computer Science and Molecular Biology. Course 6-7 prepares students for careers in emerging areas at the interface of biology and engineering — including pharmaceuticals, bioinformatics, and computational molecular biology.
  Offered jointly with the Department of Biology
• Computation and Cognition. Course 6-9 focuses on the emerging field of computational and engineering approaches to brain science, cognition, and machine intelligence.
  Offered jointly with the Department of Brain and Cognitive Sciences
• Computer Science, Economics, and Data Science. Course 6-14 builds skills in economics, computing, and data science that are increasingly valued in both the business world and academia by exploiting the substantial overlap the fields have in their reliance on game theory and mathematical modeling techniques and their use of data analytics.
  Offered jointly with the Department of Economics
• Urban Science and Planning with Computer Science. Course 11-6 emphasizes the development of fundamental skills in urban planning and policy, including ethics and justice; statistics, data science, geospatial analysis, and visualization; and computer science, robotics, and machine learning.
  Offered jointly with the Department of Urban Studies and Planning

Minors

• Computer Science. Students minoring in Computer Science develop the knowledge and skills needed to make effective use of computer science concepts and computing technology in their future careers.
• Statistics and Data Science. Interdisciplinary minor offered by the Institute for Data, Systems and Society provides students with a working knowledge base in statistics, probability, and computation, along with an ability to perform data analysis.

Many of the problem sets focus on specific topics, such as virus population dynamics, word games, optimizing routes, or simulating the movement of a Roomba. (Roomba photograph courtesy of Stephanie Booth on Flickr; virus image courtesy of the CDC; Boggle photograph courtesy of Angelina on Flickr; MIT campus map image courtesy of RahulG on Flickr.)

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Course Description

This subject is aimed at students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems. It also aims to help students, regardless of their major, to feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class will use the Python programming language.

Course Format

This course has been designed for independent study. It provides everything you will need to understand the concepts covered in the course. The materials include:

• A complete set of Lecture Videos by Prof. Guttag.
• Resources for each lecture video, such as Handouts, Slides, and Code Files.
• Recitation Videos by course TA’s to review content and problem solving techniques.
• Homework problems with sample student solutions.
• Further Study collections of links to supplemental online content.
• Self-Assessment tools, including lecture questions with answers and unit quizzes with solutions, to assess your subject mastery.

Course Info

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