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--- lectures [2011/09/27 11:15] – nick
+++ lectures [2011/12/09 19:19] (current) – nick
@@ Line 9: / Line 9: @@
      * {{:flcases.pdf|fuzzy logic cases}}
   * {{:lecture_4x.ppt|Lecture 4 - more rough sets}}
-  * {{:lecture_5a.ppt|Lecture 5 - more rough sets-Various Reducts & Rough Sets Applications}}
+  * {{:lecture_5x.ppt|Lecture 5 - more rough sets-Various Reducts & Rough Sets Applications}}
      * {{:umuai2007_aug31.pdf|application 1}}
      * {{:ohrn_thesis.pdf|application 2}}
@@ Line 18: / Line 18: @@
   * {{:lecture_6.ppt|Lecture 6 - Neural Networks}}
   * {{:lecture_7a.ppt|Lecture 7 - More on Neural Networks}}
-  * {{:cse4403lec8.pdf|Lecture 8 - Evolutionary Computing: What}}
+  * {{:lecture_7b.pdf|Lecture 7 - More on Neural Networks pdf version}}
-  * {{:cse4403lec9.pdf|Lecture 9 - Genetic Algorithms & Evolution Strategies}}
+  * {{:nn_tutorial.pdf|Lecture 7+ - Neural Networks tutorial video}}
-  * {{:cse4403lec10.pdf|Lecture 10 - Evolutionary & Genetic Programming}}
+  * {{:backpropagation.pdf|Lecture 7++ - Neural Networks - backpropagation}}
-  * {{:cse4403lec11.pdf|Lecture 11 - Probabilistic Reasoning: Why}}
+  * {{:lecture_14_copy.ppt|actually lecture 8-9 - Lecture 14 - Recurrent Neural Networks}}
-  * {{: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_14b.ppt|Lecture 14b - Neural Networks - Single Layer}}
@@ Line 30: / Line 27: @@
      * {{:lecture_14d.ppt|Lecture 14d - Neural Networks - Learning}}
      * {{: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 relationshipbetween protein functions and structure of biological networks}}
+  * {{:cse4403lec13.pdf|Lecture 15+ - Discussion on Bayesian Networks}}
-  * {{:lecture_20.ppt|Lecture 20 & 21- Ontology and Representation}}
+  * {{:lecture_15.ppt||Lecture 16 - Web Intelligence, Brain Informatics and Granular Computing}}
-  * {{:lecture_22.ppt|Lecture 22 - Course Review}}
+     * {{: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 relationshipbetween 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}}
+     * {{: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}}