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

Inhalt (Data Mining):

1. Introduction, Data Models vs. Algorithmic Models (Breiman) (pdf, ppt, pptx)
2. Uncertainty (pdf, ppt, pptx)
3. Bayesian Networks (pdf, ppt, pptx)
4. Learning Bayesian Networks (pdf, ppt pptx)
5. Decision Trees (ID3, C4.5, CART), Rule Learning, Version Spaces (pdf, ppt, pptx)
6. Association Rules, Basket Analysis, A-Priori Algorithm, Recommender Systems: Content-based Systems & Collaborative Filtering, Latent Factors (pdf, ppt, pptx)
7. Knowledge in Learning, First-Order Inductive Learning, Relational Learning (pdf, ppt, pptx)
8. Learning Probabilistic Relational Models (pdf, ppt, pptx)
9. Classification (neural networks, support vector machines) (pdf, ppt, pptx)
10. Generative vs. discriminative models (logistic regression, markov networks, conditional random fields) (pdf, ppt, pptx)
11. Augmenting Probabilistic Graphical Models with Ontology Information (pdf, ppt, pptx)
12. Ensemble Learning: Bagging, Random Forests, Boosting (AdaBoost) (pdf, ppt,, pptx)
13. Computational Learning Theory (PAC Learning, VC Dimension) (pdf, ppt, pptx)
14. Transfer Learning (pdf, ppt, pptx)
15. Unsupervised Learning: Clustering (dendrograms, k-means, kernel density estimation, Parzen windows, nearest-neighbor classification, spectral clustering), Deep Learning (dimension reduction, finding relevant features automatically) (pdf, ppt, pptx)
16. Semi-supervised (Transductive) Learning (pdf, ppt, pptx)

Inhalt (Web Mining Agents):

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

Zielgruppe:

als CS5131:

• Master MML (Wahlpflicht), Informatik, Beliebiges Fachsemester
• Master Medieninformatik SJ14 (Wahlpflicht), Informatik, Beliebiges Fachsemester
• Master Medizinische Informatik SJ14 (Wahlpflicht), eHealth / Informatik, 1. oder 2. Fachsemester

als CS5131T:

• Master MML (Modulteil eines Wahlmoduls), Informatik, Beliebiges Fachsemester
• Master Entrepreneurship in digitalen Technologien SJ14 (Modulteil eines Wahlmoduls), Modulteil, Beliebiges Fachsemester
• Master Informatik SJ14 (Modulteil eines Wahlmoduls), Modulteil, Beliebiges Fachsemester