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Course Info

  • Course Number / Code:
  • 2.16 (Spring 2006) 
  • Course Title:
  • Identification, Estimation, and Learning 
  • Course Level:
  • Graduate 
  • Offered by :
  • Massachusetts Institute of Technology (MIT)
    Massachusetts, United States  
  • Department:
  • Mechanical Engineering 
  • Course Instructor(s):
  • Prof. Harry Asada 
  • Course Introduction:
  •  


  • 2.160 Identification, Estimation, and Learning



    Spring 2006




    Course Highlights


    This course features extensive lecture notes and many assignments.


    Course Description


    This course provides a broad theoretical basis for system identification, estimation, and learning. Students will study least squares estimation and its convergence properties, Kalman filters, noise dynamics and system representation, function approximation theory, neural nets, radial basis functions, wavelets, Volterra expansions, informative data sets, persistent excitation, asymptotic variance, central limit theorems, model structure selection, system order estimate, maximum likelihood, unbiased estimates, Cramer-Rao lower bound, Kullback-Leibler information distance, Akaike's information criterion, experiment design, and model validation.


    Technical Requirements


    Special software is required to use some of the files in this course: .zip. The .txt files in the assignments section are used for MATLAB®.

     

ACKNOWLEDGEMENT:
This course content is a redistribution of MIT Open Courses. Access to the course materials is free to all users.






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