Useful handouts for this course include:

• Things You Should Know - General Course Material
  • how to study
  • paper writing handout
  • active reading handout
  • knowledge representation handout
  • logic handout

• Week One - Fuzzy Sets and Logic
  • Zadeh's original 1965 paper
  • The fuzzy logic concept
  • Zadeh's 1998 soft computing paperindexmenu_n_1
  • fuzzy logic tutorial 1
  • fuzzy logic tutorial 2
  • fuzzy logic tutorial 3
  • introduction to soft computing
  • intro to soft computing - tools
  • ten claims about fuzzy logic
  • soft computing heuristics

• Week Two and Three - Rough Sets
  • Pawlak's original 1982 article on rough sets
  • Rough sets: Some extensions
  • Pawlak's CACM rough sets
  • Pawlak's rough sets approach to decision support
  • Pawlak's probablistic vs deterministic rough sets
  • Pawlak's rough sets theory and applications
  • Rough Sets In Data Analysis: Foundations and Applications
  • Towards Scalable Algorithms for Discovering Rough Set Reducts
  • reduct construction algorithms
  • wikipedia rough sets
  • A General Definition of an Attribute Reduct
  • Attribute reduction in decision-theoretic rough set models
  • Criteria for choosing a rough set model
  • rough sets hybrid intelligent system for survival analysis
  • Rough Set software links
  • Useful Feature Subsets and Rough Set Reducts
  • Rough set methods in feature selection and recognition
  • The Rough Set Exploration System
  • Evolution of the Rough Set Exploration System
  • The New Rough Set Exploration System
  • RSES and RSESlib
  • Rossetta technical reference manual
  • Rossetta lib
  • The Rough Set ROSE System
  • Rough Set Based Model to Rank Association Rules
  • Empirical Analysis on the Geriatric Care Data Set
  • Mereological Reasoning in Rough Set Theory
  • DBROUGH - Tony Hu's phd thesis

• Week Three and Four - Neural Networks
  • neural networks
  • What is a Neural Network
  • Neural Network - simplified mathematical model
  • the backpropagation algorithm
  • two problems with bp - richard sutton
  • Intro to Neural Networks Video
  • Succinct Intro to Neural Networks
  • How Neural Networks Learn from Experience
  • |FAQ about Neural Networks
  • Probabilistic Neural Networks
  • Ensembling Neural Networks: Many Could Be Better Than All
  • What's Hidden in the Hidden Layers

• Week Four+ - Some Useful write-ups on important Topics
  • simulated annealing
  • basic intro to neural networks
  • when to use (or not) bp neural networks
  • tutorial - basic genetic algorithm in plain english
  • greedy algorithm
  • hill climbing
  • tabu search

• Week Four+ - Some Useful Papers on important Topics
  • simulated annealing
  • Optimization by Simulated Annealing
  • A tutorial survey of theory and applications of simulated anealing
  • Intro to hill climbing slides

• Week Five and Six - Evolutionary Computing
  • Introductory Survey of Evolutionary Computing - Foegel
  • Evolutionary Computing Bibliography
  • What is evolutionary computation?
  • Evolutionary computing
  • What is an evolutionary algorithm?
  • Evolutionary Programming Made Faster
  • Using evolutionary programming to create ...
  • Evolution strategies
  • Fast Evolution strategies

• Week Five and Six - Genetic Computing
  • Genetic Programming: An Introduction and Tutorial
  • Genetic Programming
  • Genetic Programming An Introductory Tutorial
  • Genetic Algorithms
  • Genetic algorithms with multi-parent recombination
  • Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms
  • A Genetic Algorithm for Constructing Compact Binary Decision Trees

• Week Six and Seven - Probabilistic Inference
  • Bayesian Networks for Clinical Decision Support
  • Bayes Offers a 'New' Way to Make Sense of Numbers
  • Seeded Bayesian Networks
  • Bayesian Networks
  • Inference in Bayesian Networks
  • Bayesian Networks without Tears
  • |Deductive reasoning
  • Explaining Inferences in Bayesian Networks
  • Inference in Belief Networks: A Procedural Guide
  • Protein Secondary Structure Modeling
  • A Tutorial on Learning With Bayesian Networks
  • A Primer on Learning in Bayesian Networks for Computational Biology
  • BAYESIAN NETWORKS
  • Belief Networks Hidden Markov Models & Markov Random Fields a Unifying
  • Inferring Cellular Networks Using Probabilistic Graphical Models

• Week Eight - Granular Computing
  • Granular Computing and Rough Sets
  • The Art of Granular Computing
  • Granular Computing: Past, Present and Future
  • Granular Computing for Data Mining
  • Attribute Reduction Based on Granular Computing
  • Information granules and entropy theory in information systems
  • Theoretical Study of Granular Computing
  • Human Inspired Granular Computing

• Week Nine - Web Intelligence, Brain Informatics and Granular Computing
  • Ways to Develop Human-Level Web Intelligence
  • Web Intelligence meets Brain Informatics
  • Web Intelligence meets Brain Informatics at the Language Barrier
  • Granular Computing for Web Intelligence and Brain Informatics
  • Reflections on Soft Computing and Granular Computing

• Week Ten - Expert Systems, Hybrid Computing Systems
  • Expert Systems: Where are we and ...
  • Expert Systems
  • rule-based expert systems
  • Extracting Knowledge from Expert Systems
  • Making Expert Systems Explicit
  • Rule-Based Expert Systems: The MYCIN Experiments
  • Integration Architecture of Expert Systems, Neural Networks, Hypertext, and Multimedia
  • Markov Algorithms

• Week Eleven and Twelve - Hybrid Intelligent Systems
  • Bayesian Network Modelling for Evolutionary Genetic Structures
  • Rough Sets Theoretical and Computational Hybrid Intelligent System for Survival Analysis
  • Finding Local Structural Differences between a Normal and Tumor Graph