====== Lectures ======

The Lectures for this course include:

  * {{:lecture_1.pdf|Lecture 1 - course introduction and fuzzy logic}}
  * {{:lecture_2x.pdf|Lecture 2 - preliminaries and fuzzy logic}}
     * {{:rs-kdd.ppt|Alternative Lecture 2 - rough sets in KDD: Tutorial Notes}}
  * {{:lecture_3x.pdf|Lecture 3 - finish fuzzy logic and begin rough sets}}
     * {{:flcases.pdf|fuzzy logic cases}}
  * {{:lecture_4x.pdf|Lecture 4 - more rough sets}}
  * {{{{:lecture_5x.pdf|Lecture 5 - more rough sets-Various Reducts & Rough Sets Applications}}
     * {{:umuai2007_aug31.pdf|application 1}}
     * {{:ohrn_thesis.pdf|application 2}}
     * {{:p1120548004.pdf|application 3}}
     * {{:camwa2008pp.pdf|application 4}}
     * {{:full3912.pdf|application 5}}
  * {{:dbrough.pdf|Lecture 5a - DBROUGH}}
  * {{:lecture_6.pdf|Lecture 6 - Neural Networks}}
  * {{:lecture_7a.pdf|Lecture 7 - More on Neural Networks}}
  * {{:nn_tutorial.pdf|Lecture 7+ - Neural Networks tutorial video}}
  * {{:backpropagation.pdf|Lecture 7++ - Neural Networks - backpropagation}}
  * {{:lecture_8.pdf|lecture 7+++ - Recurrent Neural Networks}}
     * {{:lecture_14aa.ppt|Lecture 7a - The Next Generation Neural Networks - G. Hinton}}
     * {{:lecture_14b.ppt|Lecture 7b - Neural Networks - Single Layer}}
     * {{:lecture_14c.ppt|Lecture 7c - Neural Networks - Probabilistic}}
     * {{:lecture_14d.ppt|Lecture 7d - Neural Networks - Learning}}
     * {{:lecture_14f.ppt|Lecture 7e - Neural Networks - Feed Forward}}
  * {{:cse4403lec8.pdf|Lecture 8 - Evolutionary Computing: What}}
      * {{:evolutionary_algorithm.pdf|Daniel Dennett’s lectures on evolutionary computing}}
  * {{:cse4403lec9.pdf|Lecture 9 - Genetic Algorithms & Evolution Strategies}}
  * {{:cse4403lec10.pdf|Lecture 10 - Evolutionary & Genetic Programming}}
  * {{:cse4403lec11.pdf|Lecture 11 - Probabilistic Reasoning: Why}}
  * {{:cse4403lec12.pdf|Lecture 12 - Bayesian Networks}}
  * {{:cse4403lec13.pdf|Lecture 13 - Discussion on Bayesian Networks}}
  * {{:lecture_12.pdf|Lecture 14 - Ontology and Representation}}
  * {{:lecture_16.pdf|Lecture 15 - Web Intelligence, Brain Informatics and Granular Computing}}
     * {{:wimbit.pdf|Lecture 15a - Web Intelligence meet Brain Informatics}}
  * {{:lecture_17new.pdf|Lecture 16 - Granular Computing}}
     * {{:att00207.pdf|Lecture 16a - Zadeh's Granular Computing}}
     * {{:conceptual_granularity_fuzzy_and_rough_sets.pdf|Lecture 16b - Erich's Granular Computing}}
     * {{:granular_computing.pdf|Lecture 16c - Skrowon's Granular Computing}}
  * {{:hmm_nicky.pdf|Lecture 17 - Hidden Markov Models}}
  * {{:lecture_18.pdf|Lecture 18 - Introduction to Expert Systems}}
  * {{:bayesian_network_modeling.pdf|Lecture 19 - Bayesian Network Modeling for evolutionary genetic structures}}
  * {{:cerconenew.pdf|Lecture 20 - A graph theory approach to characterize the relationshipbetween protein functions and structure of biological networks}}
  * {{:sc20130nov26short.pdf|Lecture 20a - Graph theory and biological networks}}
  * {{:lecture_22.ppt|Lecture 21 - Course Review}}

Student project presentations for this course include:

  * {{:cse4403.ppt|Noada Lugaj and Jason Panas - An investigation about the application of Artificial Neural Networks in medical diagnosis}}
  * {{:sentencesimpliprojectpresentation_nicky_mee.ppt|Ameeta Agrawal and Nikolay Yakovets - Sentence simpliFIcation using simple wikipedia}}
  * {{:4403_presentation.ppt|Albert VanderMeulen - Phoneme Recognition Using Neural Networks}}
  * {{|}}



     * {{:00-kickoff-ppt.ppt|Eiben-Smith Lectures - Kickoff}}
     * {{:01-introduction-ppt.ppt|Eiben-Smith Lectures - Introduction}}
     * {{:02-what_is_an_ea-ppt.ppt|Eiben-Smith Lectures - What is an EA}}
     * {{:03-genetic_algorithms-ppt.ppt|Eiben-Smith Lectures - Genetic Algorithms}}
     * {{:04-evolution_strategies-ppt.ppt|Eiben-Smith Lectures - Evolutionary Strategy}}
     * {{:05-evolutionary_programming-ppt.ppt|Eiben-Smith Lectures - Evolutionary Programming}}
     * {{:06-genetic_programming-ppt.ppt|Eiben-Smith Lectures - Genetic Programming}}
     * {{:08-parameter_control-ppt.ppt|Eiben-Smith Lectures - Parameter Tuning}}
     * {{:09-multi-ppt.ppt|Eiben-Smith Lectures - Multimodal Problems}}
     * {{:10-memetic_algorithms-ppt.ppt|Eiben-Smith Lectures - Memetic Algorithms}}
     * {{:11-theory-ppt.ppt||Eiben-Smith Lectures - Theory}}
     * {{:12-constraints-ppt.ppt|Eiben-Smith Lectures - Constraints}}


     * {{:http.pdf|Probabilistic inference lectures}}