Inference From Data and Models OpenCourseWare: A Free Graduate Study Course by MIT on Kinematical and Dynamical Models

Published Jan 14, 2009

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Students learn how to solve practical problems with flawed data via 'Inference from Data and Models,' which is the Massachusetts Institute of Technology's (MIT) OpenCourseWare that outlines various annotations within the realms of kinematics and dynamics. This course will cover a broad range of material that will fall under the scope of two main themes, including linear methods and standard time series analysis. Those seeking a master's degree in Physics or Earth Science will find valuable preparatory information throughout this course.

Inference from Data and Models: Course Specifics

Degree Level Free Audio Video Downloads
Graduate Yes No No Yes

Lectures/Notes Study Materials Tests/Quizzes
Yes Yes No

Inference from Data and Models: Course Description

Kinematics and dynamics cover a wide range of research data and the analysis of the relevant models is essential for further development in this field. Professor Carl Wunsch ties together two themes of study to arrive at scientifically prudent conclusions from inaccurate data. Topics begin with Fourier analysis and discrete observations and include geometric interpretation and trend determination later on. This course covers several theorems, including the Sampling Theorem and the Karhunen-Loève Theorem. Lecture notes and problem set homework assignments cover three different years of the course. Professor Wunsch recommends that students take either 'Advanced Calculus for Engineers' or 'Mathematical Methods for Engineers II' as a prerequisite. Both courses are offered by MIT as OpenCourseWare.

Lecture notes and homework assignments are offered online for free. Additional information on this course can be found at the kinematical and dynamical models course home page.

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