====== handouts ====== Useful handouts for this course include: * Things You Should Know - 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}} * {{:the_fuzzy_logic_concept.pdf|The fuzzy logic concept}} * {{:zadeh.pdf|Zadeh's 1998 soft computing paper}}{{indexmenu_n>1}} * {{: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}} * {{:markov1.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}}