






The objectives of the project is that the students understand the use of associative matrices in learning and recognizing patterns, objects, and strings such as DNA sequences in bioinformatics. After the completion of this project the student should be able to:
 Understand the use and implementation of associative matrices.
 Recognize the capability of these type of networks to associate new matrices with previously learned patterns allowing to recognize patterns with noise or different from the original patterns.
 Understand the use of associative networks and pattern recognition.
 Understand the mathematical foundations of matrix or linear algebra and its applications.
 Introduce the students to the applications of artificial intelligence, learning, and neural networks to the bioinformatics field.













The student should have basic knowledge of matrix or linear algebra. The student should have knowledge and experience of a programming language such as C++ or Matlab to implement the models and test the algorithms.













Theory and examples of the use of associative matrices and the Hebb rule, morphological associative matrices, and bioinformatics can be found in the:
 Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach, 2nd edition. Prentice Hall, Upper Saddle River, NJ, USA, 2003. Chapter 20 and Appendix A.
 JeanMichel Claverie, Cedric Nothedame. Bionformatics for Dummies, 1st Edition. For Dummies, 2003. Chapters 1 and 2.













The detailed project description is available in the PDF file CompleteProject.pdf. You will need the free Adobe Acrobat Reader to view this file.


This project is customizable to accommodate different approaches to teaching and different implementations. Additional exercises are also included for students seeking more extended challenges.









