Stochastic Processes, Detection and Estimation OpenCourseWare: A Free MIT Graduate Level Course
'Stochastic Processes, Detection and Estimation' is a free course offered by the Massachusetts Institute of Technology (MIT) that provides an introduction to the basics of detection and estimation theory concerning system and signal models with some inherent randomness. This OpenCourseWare from MIT is suited to graduate students in Computer Science or Electrical Engineering.
Stochastic Processes, Detection and Estimation: Course Specifics
| Degree Level | Free | Audio | Video | Downloads |
|---|---|---|---|---|
| Graduate | Yes | No | No | Yes |
| Lectures/Notes | Study Materials | Tests/Quizzes |
|---|---|---|
| Yes | Yes | Yes |
Stochastic Processes, Detection and Estimation: Course Description
This OpenCourseWare from the Massachusetts Institute of Technology teaches the basics of detection and estimation for communications, signal processing and control. The topics you'll learn about include Boyesian and Neyman-Pearson hypothesis testing, vector spaces of random variables and detection and estimation from wave form observations. Students also learn about representations for stochastic processes, including Karhunen-Loeve expansions and shaping and whitening filters. Taught by MIT Professors Alan Willsky and Gregory Wornell, this course for graduate students also covers advanced topics, such as Wiener and Kalman filters and linear prediction and spectral estimation. The concepts you'll learn are the foundation for a wide variety of algorithms used by many applications. The focus is not on these applications but rather on the problem-solving framework they have in common. If you want to take this course, you should be an expert in basic linear algebra, basic probability and discrete- and continuous-time linear systems. You should also have a basic knowledge of random signals and their manipulation.
This OpenCourseWare about stochastic processes, detection and estimation includes problem sets and recitation notes. If you are interested in taking this free course, visit the stochastic processes, detection and estimation course page.
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