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Course Info

  • Course Number / Code:
  • 18.465 (Spring 2007) 
  • Course Title:
  • Topics in Statistics: Statistical Learning Theory 
  • Course Level:
  • Graduate 
  • Offered by :
  • Massachusetts Institute of Technology (MIT)
    Massachusetts, United States  
  • Department:
  • Mathematics 
  • Course Instructor(s):
  • Prof. Dmitry Panchenko 
  • Course Introduction:
  •  


  • 18.465 Topics in Statistics: Statistical Learning Theory



    Spring 2007




    Course Highlights




    18.465 Topics in Statistics: Statistical Learning Theory



    Spring 2007


    Image of Talagrand's convex-hull distance on the cube.
    d2 represents Talagrand's convex-hull distance on the cube. (Image by Prof. Dmitry Panchenko.)


    Course Description


    The main goal of this course is to study the generalization ability of a number of popular machine learning algorithms such as boosting, support vector machines and neural networks. Topics include Vapnik-Chervonenkis theory, concentration inequalities in product spaces, and other elements of empirical process theory.
     

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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