Randomized Algorithms OpenCourseWare: MIT's Free Graduate Level Course on Algorithm Theory
Expand your understanding of algorithms with 'Randomized Algorithms,' an OpenCourseWare offered by MIT that explores how algorithms can be developed into more straightforward and effective modes of data processing. This free, graduate-level course guides students through a wide range of randomized algorithms and their related theories. This class is beneficial to those considering a master's degree in Electrical Engineering or Computer Science.
Randomized Algorithms: Course Specifics
| Degree Level | Free | Audio | Video | Downloads |
|---|---|---|---|---|
| Graduate | Yes | No | No | Yes |
| Lectures/Notes | Study Materials | Tests/Quizzes |
|---|---|---|
| Yes | Yes | No |
Randomized Algorithms: Course Description
Believe it or not, using random samples of data and other uses of randomization can actually improve algorithm performance. Professor David Karger leads students through a study of this concept that will touch upon a wide range of its benefits and applications. Students will begin with topics such as complexity theory and Adelman's Theorem and will later study hashing, shortest path models and parallel algorithms. This OpenCourseWare will be presented in lecture format and is aimed at giving the students exposure to a variety of general topics related to randomized algorithms. These topics include linear programming, convex hulls, random sampling methods and Markov chains. Students will also be presented with different applications for randomized algorithms, including the solving of large-scale computational problems, like the design of an airfoil for NASA, for example. Offered at the graduate level, the information presented in this course will be ideal for anyone pursing and advanced degree such as a Master of Science in Electrical Engineering.
Lecture notes and homework assignments are the main materials for this OpenCourseWare and are available online. If you would like more information about this OpenCourseWare, visit the randomized algorithms course home page.
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