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

Für pptx sollte der Font Myriad Pro installiert sein.

Inhalt (Data Mining - Dr. Özgür Özcep):

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 (ID3, C4.5, CART), Rule Learning, Version Spaces (pdf, pptx)
6. Learning Probabilistic Relational Models (pdf, pptx)
7. Classification (neural networks, support vector machines) (pdf, pptx)
8. Generative vs. discriminative models (logistic regression, markov networks, conditional random fields) (pdf, pptx)
9. Augmenting Probabilistic Graphical Models with Ontology Information (pdf, pptx)
10. Ensemble Learning: Bagging, Random Forests, Boosting (AdaBoost) (pdf, pptx)
11. Computational Learning Theory (pdf, pptx)
12. Unsupervised and Semi-Supervised Learning: Clustering (dendrograms, k-means, CURE, spectral clustering) (pdf, pptx)
13. Stream data mining (pdf, pptx)

Inhalt (Web Mining Agents - Prof. Möller):

1. Introduction (pdf, pptx)
2. Information Retrieval, Vector Space Model, Latent Semantic Indexing, Relational Latent Representations (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. Decision Making (Markov Decision Problem, MDP), Decision Making Under Uncertainty (Partially Observable Markov Decision Problem, POMDP) (pdf, pptx)
8. Reinforcement and Adaptation (pdf, no ppt for this part)
9. Game Theory and Social Choice (pdf, pptx)
10. Mechanism Design (pdf, pptx)
11. Reward Shaping (pdf, pptx, not in WS16/17)
12. Collaboration, Rules of Encounter (pdf, pptx, not in WS16/17)
13. Transfer of Learnt Skills

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