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Course Info

  • Course Number / Code:
  • 9.52 (Spring 2006) 
  • Course Title:
  • Statistical Learning Theory and Applications 
  • Course Level:
  • Graduate 
  • Offered by :
  • Massachusetts Institute of Technology (MIT)
    Massachusetts, United States  
  • Department:
  • Brain and Cognitive Sciences 
  • Course Instructor(s):
  • Prof. Tomaso Poggio 
  • Course Introduction:
  •  


  • 9.520 Statistical Learning Theory and Applications



    Spring 2006




    Course Highlights


    This course features extensive lecture notes. The assignments focus on some of the functions needed to make problem-solving more efficient for computer systems.


    Course Description


    This course is for upper-level graduate students who are planning careers in computational neuroscience. This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. It develops basic tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory. It also discusses topics such as boosting and feature selection and examines applications in several areas: Computer Vision, Computer Graphics, Text Classification, and Bioinformatics. The final projects, hands-on applications, and exercises are designed to illustrate the rapidly increasing practical uses of the techniques described throughout the course.



    Technical Requirements


    Special software is required to use some of the files in this course: .ps


    *Some translations represent previous versions of courses.

     

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