CSE4403

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====== handouts ======

Useful handouts for this course include:

  * General Course Material
       * {{:how_to_study.pdf|how to study}}
       * {{:paper_writing.pdf|paper writing handout}}
       * {{:active_reading.pdf|active reading handout}}
       * {{:kr.pdf|knowledge representation handout}}
       * {{:logic.pdf|logic handout}}
 
  * Week One - Fuzzy Sets and Logic
       * {{:zadeh65.pdf|Zadeh's original 1965 paper}}
       * {{:zadeh.pdf|Zadeh's 1998 soft computing paper}}
       * {{:fuzzy_logic.stanford.pdf|fuzzy logic tutorial 1}}
       * {{:fuzzytutorial.pdf|fuzzy logic tutorial 2}}
       * {{:tutorial.pdf|fuzzy logic tutorial 3}}
       * {{:soft-computing.pdf|introduction to soft computing}}
       * {{:azvine.pdf|intro to soft computing - tools}}
       * {{:hajek.pdf|ten claims about fuzzy logic}}
       * {{:jose.pdf|soft computing heuristics}}

  * Week Two and Three - Rough Sets
       * {{:rough_sets.pdf|Pawlak's original 1982 article on rough sets}}
       * {{:extensions.pdf|Rough sets: Some extensions}}
       * {{:p88-pawlak.pdf|Pawlak's CACM rough sets}}
       * {{:pawlak4.pdf|Pawlak's rough sets approach to decision support}}
       * {{:prob_vs_determ.pdf|Pawlak's probablistic vs deterministic rough sets}}
       * {{:rstheory_and_apps.pdf|Pawlak's rough sets theory and applications}}
       * {{:foundations.pdf|Rough Sets In Data Analysis: Foundations and Applications}}
       * {{:scalable.pdf|Towards Scalable Algorithms for Discovering Rough Set Reducts}}
       * {{:three_reduct.pdf|reduct construction algorithms}}
       * {{:rough_set_-_wikipedia_the_free_encyclopedia.pdf|wikipedia rough sets}}
       * {{:a_definition.pdf|A General Definition of an Attribute Reduct}}
       * {{:attr_reduction.pdf|Attribute reduction in decision-theoretic rough set models}}
       * {{:chooing_a_rs_model.pdf|Criteria for choosing a rough set model}}
       * {{:puntip1.pdf|rough sets hybrid intelligent system for survival analysis}}
       * {{:rough_sets_software.pdf|Rough Set software links}}
       * {{:rough.pdf|Useful Feature Subsets and Rough Set Reducts}}
       * {{:roughsetmethods.pdf|Rough set methods in feature selection and recognition}}
       * {{:rses_trs.pdf|The Rough Set Exploration System}}
       * {{:evolution.pdf|Evolution of the Rough Set Exploration System}}
       * {{:gmalvernbazan1.pdf|The New Rough Set Exploration System}}
       * {{:gbazan00rses.pdf|RSES and RSESlib}}
       * {{:manual.pdf|Rossetta technical reference manual}}
       * {{:classes.pdf|Rossetta lib}}
       * {{:rose.pdf|The Rough Set ROSE System}}
       * {{:rsfdgrcjiyecamera.pdf|Rough Set Based Model to Rank Association Rules}}
       * {{:techreportmedicaldata.pdf|Empirical Analysis on the Geriatric Care Data Set}}
       * {{:lech.pdf|Mereological Reasoning in Rough Set Theory}}
       * {{:huphd.pdf|DBROUGH - Tony Hu's phd thesis}}

  * Week Three and Four - Neural Networks
        * {{:neural_network_-_wikipedia_the_free_encyclopedia.pdf|neural networks}}
        * {{:artic.pdf|What is a Neural Network}}
        * {{:ann_-_math_model.pdf|Neural Network - simplified mathematical model}}
        * {{:chapter3_-_bp.pdf|the backpropagation algorithm}}
        * {{:sutton-86.pdf|two problems with bp - richard sutton}}
        * {{:nn_tutorial.pdf|Intro to Neural Networks Video}}
        * {{:report.pdf|Succinct Intro to Neural Networks}}
        * {{:sciam92.pdf|How Neural Networks Learn from Experience}}
        * {{:faq.pdf||FAQ about Neural Networks}}
        * {{:specht1990pnn.pdf|Probabilistic Neural Networks}}
        * {{:ai.pdf|Ensembling Neural Networks: Many Could Be Better Than All}}
        * {{:byte-hiddenlayer-1989.pdf|What's Hidden in the Hidden Layers}}

  * Week Four+ - Some Useful write-ups on important Topics
        * {{:simulated_annealingw.pdf|simulated annealing}}
        * {{:basic_neural_networks.pdf|basic intro to neural networks}}
        * {{:bp_neural_networks.pdf|when to use (or not) bp neural networks}}
        * {{:basic_ngenetic_algorithm.pdf|tutorial - basic genetic algorithm in plain english}}
        * {{:greedy_algorithm.pdf|greedy algorithm}}
        * {{:hill_climbinbg.pdf|hill climbing}}
        * {{:tabu_search.pdf|tabu search}}
 
  * Week Four+ - Some Useful Papers on important Topics
        * {{:simulated_annealing.pdf|simulated annealing}}
        * {{:kgv1983.pdf|Optimization by Simulated Annealing}}
        * {{:04048399.pdf|A tutorial survey of theory and applications of simulated anealing}}
        * {{:hillclimb02.pdf|Intro to hill climbing slides}}

  * Week Five and Six - Evolutionary Computing
       * {{:0780334817.pdf|Introductory Survey of Evolutionary Computing - Foegel}}
       * {{:ecbiblio.pdf|Evolutionary Computing Bibliography}}
       * {{:ec1.pdf|What is evolutionary computation?}}
       * {{:ec2.pdf|Evolutionary computing}}
       * {{:ec3.pdf|What is an evolutionary algorithm?}}
       * {{:ep1.pdf|Evolutionary Programming Made Faster}}
       * {{:ep2.pdf|Using evolutionary programming to create ...}}
       * {{:es1.pdf|Evolution strategies}}
       * {{:es2.pdf|Fast Evolution strategies}}
 
  * Week Five and Six - Genetic Computing
       * {{:gp1.pdf|Genetic Programming: An Introduction and Tutorial}}
       * {{:gp2.pdf|Genetic Programming}}
       * {{:gp3.pdf|Genetic Programming An Introductory Tutorial}}
       * {{:genetic_algorithms.pdf|Genetic Algorithms}}
       * {{:ga1.pdf|Genetic algorithms with multi-parent recombination}}
       * {{:ga2.pdf|Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms}}
       * {{:ga3.pdf|A Genetic Algorithm for Constructing Compact Binary Decision Trees}}
 
  * Week Six and Seven - Probabilistic Inference
       * {{:bayenefoc.pdf|Bayesian Networks for Clinical Decision Support}}
       * {{:bayes.science99.pdf|Bayes Offers a 'New' Way to Make Sense of Numbers}}
       * {{:bmc.pdf|Seeded Bayesian Networks}}
       * {{:bn.pdf|Bayesian Networks}}
       * {{:cbibn.pdf|Inference in Bayesian Networks}}
       * {{:charniak_91.pdf|Bayesian Networks without Tears}}
       * {{:deductive.pdf||Deductive reasoning}}
       * {{:ebi_apin.pdf|Explaining Inferences in Bayesian Networks}}
       * {{:ijar95.pdf|Inference in Belief Networks: A Procedural Guide}}
       * {{:ismb93.pdf|Protein Secondary Structure Modeling}}
       * {{:learning.pdf|A Tutorial on Learning With Bayesian Networks}}
       * {{:pcbi.pdf|A Primer on Learning in Bayesian Networks for Computational Biology}}
       * {{:pearl.pdf|BAYESIAN NETWORKS}}
       * {{:prl.pdf|Belief Networks Hidden Markov Models & Markov Random Fields a Unifying}}
       * {{:science-2004-friedman.pdf|Inferring Cellular Networks Using Probabilistic Graphical Models}}

  * Week Eight - Granular Computing
       * {{:tylin.pdf|Granular Computing and Rough Sets}}
       * {{:art_of_grc.pdf|The Art of Granular Computing}}
       * {{:ieee_grc_08.pdf|Granular Computing: Past, Present and Future}}
       * {{:grcfordm06.pdf|Granular Computing for Data Mining}}
       * {{:hu.pdf|Attribute Reduction Based on Granular Computing}}
       * {{:liang.pdf|Information granules and entropy theory in information systems}}
       * {{:liu.pdf|Theoretical Study of Granular Computing}}
       * {{:human-inspired_granular_computing.pdf|Human Inspired Granular Computing}}

  * Week Nine - Web Intelligence, Brain Informatics and Granular Computing
       * {{:ning.pdf|Ways to Develop Human-Level Web Intelligence}}
       * {{:wimbibook.pdf|Web Intelligence meets Brain Informatics}}
       * {{:wimbi.pdf|Web Intelligence meets Brain Informatics at the Language Barrier}}
       * {{:yyao2007.pdf|Granular Computing for Web Intelligence and Brain Informatics}}
       * {{:zadehgc.pdf|Reflections on Soft Computing and Granular Computing}}

  * Week Ten - Expert Systems, Hybrid Computing Systems
       * {{:aim-665.pdf|Expert Systems: Where are we and ...}}
       * {{:expert_system.pdf|Expert Systems}}
       * {{:fuzzy_chapter.pdf|rule-based expert systems}}
       * {{:newell.pdf|Extracting Knowledge from Expert Systems}}
       * {{:newell1.pdf|Making Expert Systems Explicit}}
       * {{:newell2.pdf|Rule-Based Expert Systems: The MYCIN Experiments}}
       * {{:sdarticle.pdf|Integration Architecture of Expert Systems, Neural Networks, Hypertext, and Multimedia}}
       * {{:markov.pdf|Markov Algorithms}}

 
  * Week Eleven and Twelve - Hybrid Intelligent Systems
       * {{:lisaarticle.pdf|Bayesian Network Modelling for Evolutionary Genetic Structures}}
       * {{:camwa2008pp.pdf|Rough Sets Theoretical and Computational Hybrid Intelligent System for Survival Analysis}}
       * {{|Finding Local Structural Differences between a Normal and Tumor Graph}}