The Lectures for this course include:

• Lecture 1 - course introduction and fuzzy logic
• Lecture 2 - preliminaries and fuzzy logic
  • Alternative Lecture 2 - rough sets in KDD: Tutorial Notes
• Lecture 3 - finish fuzzy logic and begin rough sets
  • fuzzy logic cases
• Lecture 4 - more rough sets
• Lecture 5 - more rough sets-Various Reducts & Rough Sets Applications
  • application 1
  • application 2
  • application 3
  • application 4
  • application 5
• Lecture 5a - DBROUGH
• Lecture 6 - Neural Networks
• Lecture 7 - More on Neural Networks
• Lecture 7 - More on Neural Networks pdf version
• Lecture 7+ - Neural Networks tutorial video
• Lecture 7++ - Neural Networks - backpropagation
• Lecture 8 - Evolutionary Computing: What
  • Daniel Dennett’s lectures on evolutionary computing
• Lecture 9 - Genetic Algorithms & Evolution Strategies
• Lecture 10 - Evolutionary & Genetic Programming
• Lecture 11 - Probabilistic Reasoning: Why
• Lecture 12 - Bayesian Networks
• Lecture 13 - Discussion on Bayesian Networks
• Lecture 14 - Recurrent Neural Networks
  • Lecture 14a - The Next Generation Neural Networks - G. Hinton
  • Lecture 14b - Neural Networks - Single Layer
  • Lecture 14c - Neural Networks - Probabilistic
  • Lecture 14d - Neural Networks - Learning
  • Lecture 14e - Neural Networks - Feed Forward
• |Lecture 15 - Web Intelligence, Brain Informatics and Granular Computing
  • Lecture 15a - Web Intelligence meet Brain Informatics
• Lecture 16 - Granular Computing
  • Lecture 16a - Zadeh's Granular Computing
  • Lecture 16 - Erich's Granular Computing
  • Lecture 16c - Skrowon's Granular Computing
• Lecture 17 - Introduction to Expert Systems
• Lecture 18 - Bayesian Network Modeling for evolutionary genetic structures
• Lecture 19 - A graph theory approach to characterize the relationshipbetween protein functions and structure of biological networks
• Lecture 20 & 21- Ontology and Representation
• Lecture 22 - Course Review