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lectures [2011/11/09 15:23] – nicklectures [2011/12/09 19:19] (current) – nick
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   * {{:nn_tutorial.pdf|Lecture 7+ - Neural Networks tutorial video}}   * {{:nn_tutorial.pdf|Lecture 7+ - Neural Networks tutorial video}}
   * {{:backpropagation.pdf|Lecture 7++ - Neural Networks - backpropagation}}   * {{:backpropagation.pdf|Lecture 7++ - Neural Networks - backpropagation}}
-  * {{:cse4403lec8.pdf|Lecture 8 - Evolutionary Computing: What}} +  * {{:lecture_14_copy.ppt|actually lecture 8-9 - Lecture 14 - Recurrent Neural Networks}}
-      * {{: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}} +
-  * {{:lecture_20.ppt|Lecture 20 & 21- Ontology and Representation}} +
-  * {{:hmm_nicky.pdf|Lecture 13 - Hidden Markov Models}} +
-  * {{:cse4403lec11.pdf|Lecture 11 - Probabilistic Reasoning: Why}} +
-  * {{:cse4403lec12.pdf|Lecture 12 - Bayesian Networks}} +
-  * {{:cse4403lec13.pdf|Lecture 13 - Discussion on Bayesian Networks}} +
-  * {{:lecture_14_copy.ppt|Lecture 14 - Recurrent Neural Networks}}+
      * {{:lecture_14aa.ppt|Lecture 14a - The Next Generation Neural Networks - G. Hinton}}      * {{:lecture_14aa.ppt|Lecture 14a - The Next Generation Neural Networks - G. Hinton}}
      * {{:lecture_14b.ppt|Lecture 14b - Neural Networks - Single Layer}}      * {{:lecture_14b.ppt|Lecture 14b - Neural Networks - Single Layer}}
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      * {{:lecture_14d.ppt|Lecture 14d - Neural Networks - Learning}}      * {{:lecture_14d.ppt|Lecture 14d - Neural Networks - Learning}}
      * {{:lecture_14f.ppt|Lecture 14e - Neural Networks - Feed Forward}}      * {{:lecture_14f.ppt|Lecture 14e - Neural Networks - Feed Forward}}
-  * {{:lecture_15.ppt||Lecture 15 - Web Intelligence, Brain Informatics and Granular Computing}} +  * {{:cse4403lec8.pdf|Lecture 10 - Evolutionary Computing: What}} 
-     * {{:wimbit.ppt|Lecture 15a - Web Intelligence meet Brain Informatics}} +      * {{:evolutionary_algorithm.pdf|Daniel Dennett’s lectures on evolutionary computing}} 
-  * {{:lecture_16.ppt|Lecture 16 - Granular Computing}} +  * {{:cse4403lec9.pdf|Lecture 11 - Genetic Algorithms & Evolution Strategies}} 
-     * {{:att00207.ppt|Lecture 16a - Zadeh's Granular Computing}} +  * {{:cse4403lec10.pdf|Lecture 12 - Evolutionary & Genetic Programming}} 
-     * {{:conceptual_granularity_fuzzy_and_rough_sets.ppt|Lecture 16 - Erich's Granular Computing}} +  * {{:lecture_20.ppt|actually Lecture 13 - Lecture 20 & 21- Ontology and Representation}} 
-     * {{:granular_computing.pdf|Lecture 16c - Skrowon's Granular Computing}} +  * {{:hmm_nicky.pdf|Lecture 14 - Hidden Markov Models}} 
-  * {{:lecture_17.ppt|Lecture 17 - Introduction to Expert Systems}} +  * {{:cse4403lec11.pdf|Lecture 11 - Probabilistic Reasoning: Why}} 
-  * {{:bayesian_network_modeling_for_evolutionary_genetic_structures.ppt|Lecture 18 - Bayesian Network Modeling for evolutionary genetic structures}} +  * {{:cse4403lec12.pdf|Lecture 15 - Bayesian Networks}} 
-  * {{:cercone.ppt|Lecture 19 - A graph theory approach to characterize the relationship between protein functions and structure of biological networks}} +  * {{:cse4403lec13.pdf|Lecture 15+ - Discussion on Bayesian Networks}} 
-  * {{:lecture_22.ppt|Lecture 22 - Course Review}}+  * {{:lecture_15.ppt||Lecture 16 - Web Intelligence, Brain Informatics and Granular Computing}} 
 +     * {{:wimbit.ppt|Lecture 16a - Web Intelligence meet Brain Informatics}} 
 +  * {{:lecture_16.ppt|Lecture 17 - Granular Computing}} 
 +     * {{:att00207.ppt|Lecture 17a - Zadeh's Granular Computing}} 
 +     * {{:conceptual_granularity_fuzzy_and_rough_sets.ppt|Lecture 17b - Erich's Granular Computing}} 
 +     * {{:granular_computing.pdf|Lecture 17c - Skrowon's Granular Computing}} 
 +  * {{:lecture_17.ppt|Lecture 18 - Introduction to Expert Systems}} 
 +  * {{:bayesian_network_modeling_for_evolutionary_genetic_structures.ppt|Lecture 19 - Bayesian Network Modeling for evolutionary genetic structures}} 
 +  * {{:cercone.ppt|Lecture 20 - A graph theory approach to characterize the relationship between protein functions and structure of 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}}      * {{:00-kickoff-ppt.ppt|Eiben-Smith Lectures - Kickoff}}
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      * {{:11-theory-ppt.ppt||Eiben-Smith Lectures - Theory}}      * {{:11-theory-ppt.ppt||Eiben-Smith Lectures - Theory}}
      * {{:12-constraints-ppt.ppt|Eiben-Smith Lectures - Constraints}}      * {{:12-constraints-ppt.ppt|Eiben-Smith Lectures - Constraints}}
 +
 +
 +     * {{:http.pdf|Probabilistic inference lectures}}
  
  
lectures.1320852219.txt.gz · Last modified: by nick