Web-Mining Agents (CS5131)

Lecturer: Prof. Dr. rer.nat. Ralf Möller

Contents (Data Mining - Dr. Özgür Öczep):

  1. Introduction, data models vs. algorithmic models (Breiman) (pdf, pptx)
  2. Uncertainty (pdf, pptx)
  3. Bayesian Networks (pdf, pptx)
  4. Learning Bayesian Networks (pdf, pptx)
  5. Decision Trees, Rule Learning, Version Spaces (pdf, pptx)
  6. Knowledge in Learning, First-Order Inductive Learning, Relational Learning (pdf, pptx)
  7. Learning Probabilistic Relational Models (pdf, pptx)
  8. Classification (neural networks, support vector machines) (pdf, pptx)
  9. Generative vs. discriminative models (logistic regression, markov networks, conditional random fields) (pdf, pptx)
  10. Augmenting Probabilistic Graphical Models with Ontology Information (pdf, pptx)
  11. Ensemble Learning: Bagging, Random Forests, Boosting (AdaBoost) (pdf, pptx)
  12. Transfer Learning (pdf, pptx)
  13. Unsupervised Learning: Clustering (dendrograms, k-means, kernel density estimation, Parzen windows, nearest-neighbor classification, spectral clustering) (pdf, pptx)

Contents (Web Mining Agents - Prof. Dr. Möller):

  1. Introduction (pdf, pptx)
  2. Information Retrieval, Vector Space Model (pdf, pptx)
  3. Probabilistic Information Retrieval (pdf, pptx)
  4. Probabilistic Reasoning Over Time (pdf, pptx)
  5. Topic Detection (pdf, pptx)
  6. Community Analysis (pdf, pptx)
  7. Simple Decision Making (pdf, pptx)
  8. Complex Decision Making (Markov Decision Problem, MDP), Decision Making Under Uncertainty (Partially Observable Markov Decision Problem, POMDP) (pdf, pptx)
  9. Reinforcement and Adaptation (pdf, no ppt for this part)
  10. Transfer in Reinforcement Learning (pdf, pptx)
  11. Game Theory and Social Choice (pdf, pptx)
  12. Mechanism Design and Collaboration (pdf, pptx)