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This shows you the differences between two versions of the page.

--- lectures [2013/10/21 16:35] – nick
+++ lectures [2013/11/26 14:43] (current) – nick
@@ Line 20: / Line 20: @@
   * {{:nn_tutorial.pdf|Lecture 7+ - Neural Networks tutorial video}}
   * {{:backpropagation.pdf|Lecture 7++ - Neural Networks - backpropagation}}
-  * {{:lecture_8.pdf|lecture 8 - Recurrent Neural Networks}}
+  * {{:lecture_8.pdf|lecture 7+++ - Recurrent Neural Networks}}
-     * {{:lecture_14aa.ppt|Lecture 14a - The Next Generation Neural Networks - G. Hinton}}
+     * {{:lecture_14aa.ppt|Lecture 7a - The Next Generation Neural Networks - G. Hinton}}
-     * {{:lecture_14b.ppt|Lecture 14b - Neural Networks - Single Layer}}
+     * {{:lecture_14b.ppt|Lecture 7b - Neural Networks - Single Layer}}
-     * {{:lecture_14c.ppt|Lecture 14c - Neural Networks - Probabilistic}}
+     * {{:lecture_14c.ppt|Lecture 7c - Neural Networks - Probabilistic}}
-     * {{:lecture_14d.ppt|Lecture 14d - Neural Networks - Learning}}
+     * {{:lecture_14d.ppt|Lecture 7d - Neural Networks - Learning}}
-     * {{:lecture_14f.ppt|Lecture 14e - Neural Networks - Feed Forward}}
+     * {{: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}}
-  * {{:lecture_20.ppt|actually Lecture 13 - Lecture 20 & 21- Ontology and Representation}}
-  * {{:hmm_nicky.pdf|Lecture 14 - Hidden Markov Models}}
   * {{:cse4403lec11.pdf|Lecture 11 - Probabilistic Reasoning: Why}}
-  * {{:cse4403lec12.pdf|Lecture 15 - Bayesian Networks}}
+  * {{:cse4403lec12.pdf|Lecture 12 - Bayesian Networks}}
-  * {{:cse4403lec13.pdf|Lecture 15+ - Discussion on Bayesian Networks}}
+  * {{:cse4403lec13.pdf|Lecture 13 - Discussion on Bayesian Networks}}
-  * {{:lecture_15.ppt||Lecture 16 - Web Intelligence, Brain Informatics and Granular Computing}}
+  * {{:lecture_12.pdf|Lecture 14 - Ontology and Representation}}
-     * {{:wimbit.ppt|Lecture 16a - Web Intelligence meet Brain Informatics}}
+  * {{:lecture_16.pdf|Lecture 15 - Web Intelligence, Brain Informatics and Granular Computing}}
-  * {{:lecture_16.ppt|Lecture 17 - Granular Computing}}
+     * {{:wimbit.pdf|Lecture 15a - Web Intelligence meet Brain Informatics}}
-     * {{:att00207.ppt|Lecture 17a - Zadeh's Granular Computing}}
+  * {{:lecture_17new.pdf|Lecture 16 - Granular Computing}}
-     * {{:conceptual_granularity_fuzzy_and_rough_sets.ppt|Lecture 17b - Erich's Granular Computing}}
+     * {{:att00207.pdf|Lecture 16a - Zadeh's Granular Computing}}
-     * {{:granular_computing.pdf|Lecture 17c - Skrowon's Granular Computing}}
+     * {{:conceptual_granularity_fuzzy_and_rough_sets.pdf|Lecture 16b - Erich's Granular Computing}}
-  * {{:lecture_17.ppt|Lecture 18 - Introduction to Expert Systems}}
+     * {{:granular_computing.pdf|Lecture 16c - Skrowon's Granular Computing}}
-  * {{:bayesian_network_modeling_for_evolutionary_genetic_structures.ppt|Lecture 19 - Bayesian Network Modeling for evolutionary genetic structures}}
+  * {{:hmm_nicky.pdf|Lecture 17 - Hidden Markov Models}}
-  * {{:cercone.ppt|Lecture 20 - A graph theory approach to characterize the relationshipbetween protein functions and structure of biological networks}}
+  * {{: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}}