Teaching materials for Statistical Pattern Recognition

1. General

2. Specific Methods

   1. Distance Metrics
   2. Bayesian Methods
   3. Sequential Classifiers
   4. Discriminating Feature Methods
   5. Fuzzy
   6. K-Nearest Neighbor
   7. Linear and Higer Order Discriminant Functions
   8. Minimum Distance
   9. Multi-classifier fusion
   10. Neural Network and Perceptron Methods
   11. Markov Random Field
   12. Genetic Algorithm
   13. Nonparametric Estimation
   14. Support Vector Machine
   15. Expectation Maximization

Details of the content

1. General
   

   Notes	Slides	Reading	Homework
   Andrew Webb A.K. Jain	Jim Rehq Dorothea  Blostein	Jim Rehq Dorothea  Blostein	Jim Rehq Dorothea  Blostein

   
   

2. Specific Methods

   1. Distance Metrics
      

      Notes	Slides	Reading	Homework
      Sargur N. Srihari	Sargur N. Srihari

      
      
   2. Bayesian Method
      

      Notes	Slides	Reading	Homework
      Kevin Murphy	Kevin Murphy Amos Storkey	Tom Mitchel	Kevin Murphy

      
      
   3. Sequential Classifiers
      

      Notes	Slides	Reading	Homework
      	Sargur N. Srihari

      
      
   4. Discriminating Feature Methods
      

      Notes	Slides	Reading	Homework
      	Chengjun Liu

      
      
   5. Fuzzy
      

      Notes	Slides	Reading	Homework
      Hans U.F.N. Christensen	Hans U.F.N. Christensen	Hans U.F.N. Christensen	Hans U.F.N. Christensen

      
      
   6. K-Nearest Neighbor
      

      Notes	Slides	Reading	Homework
      Kardi Teknomo	Philippe Simard

      
      
   7. Linear and Higer Order Discriminant Functions
      

      Notes	Slides	Reading	Homework

      
      
   8. Minimum Distance
      

      Notes	Slides	Reading	Homework

      
      
   9. Multi-classifier fusion
      

      Notes	Slides	Reading	Homework
      Thomas Dietterich	Samy Bengio	Robert Schapire

      
      
   10. Neural Network and Perceptrion Methods
       

       Notes	Slides	Reading	Homework
       Ingrid F. Russel	Genevieve Orr Andrew Moore	Amos Storkey	Tom Mitchel

       
       
   11. Markov Random Field
       

       Notes	Slides	Reading	Homework
       Brani Vidakovic Kevin Murphy	Charles A. Bouman	Fabio Ramos	Kevin Murphy

       
       
   12. Genetic Algorithm
       

       Notes	Slides	Reading	Homework
       Janne Koljonen  Peter Ross	Gillian Hayes	Darrel Whitley	Gillian Hayes

   13. Nonparametric Estimation
       

       Notes	Slides	Reading	Homework
       	Sargur N. Srihari

   14. Support Vector Machine

       Notes	Slides	Reading	Homework
       Max Welling	Andrew Moore	Chris Burges	Tom Mitche

       
       
   15. Expectation Maximization
       

       Notes	Slides	Reading	Homework
       Andrew Ng	Tommi Jaakola	Neal and Hinton	Kevin Murphy

       
       

Return to Educator Resource page

© 2008 Robert Fisher