Key Information
About the content
6.00.2x will teach you how to use computation to accomplish a variety of goals and provides you with a brief introduction to a variety of topics in computational problem solving . This course is aimed at students with some prior programming experience in Python and a rudimentary knowledge of computational complexity. You will spend a considerable amount of time writing programs to implement the concepts covered in the course. For example, you will write a program that will simulate a robot vacuum cleaning a room or will model the population dynamics of viruses replicating and drug treatments in a patient's body.
Topics covered include:
- Advanced programming in Python 3
- Knapsack problem, Graphs and graph optimization
- Dynamic programming
- Plotting with the pylab package
- Random walks
- Probability, Distributions
- Monte Carlo simulations
- Curve fitting
- Statistical fallacies
Prerequisite
6.00.1x or equivalent (some prior programming experience in Python and a rudimentary knowledge of computational complexity)
Syllabus
- Plotting with the pylab package
- Stochastic programming and statistical thinking
- Monte Carlo simulations
Instructors
John Guttag
Dugald C. Jackson Professor of Computer Science and Electrical Engineering
MIT
Eric Grimson
Bernard Gordon Professor of Medical Engineering, Professor of Computer Science
MIT
Ana Bell
Senior Lecturer, Computer Science and Electrical Engineering
Massachusetts Institute of Technology
Content Designer
MIT is a world-class educational institution where teaching and research — with relevance to the practical world as a guiding principle — continue to be its primary purpose.
MIT is independent, coeducational, and privately endowed. Its five schools and one college encompass numerous academic departments, divisions and degree-granting programs, as well as interdisciplinary centers, laboratories and programs whose work cuts across traditional departmental boundaries.
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