Date |
Slides |
Topics |
Literature |
Lecturer |
Comments |
|---|---|---|---|---|---|
Chapter I: Introduction | |||||
| October 18 | IR overview: [PDF] [PPTX];
Part on data mining: [PDF] |
IRDM applications & demos | Pauli & Martin | Not relevant for the exam | |
Chapter II: Basics from probability theory and statistics | |||||
| October 20 | [PDF] [PPTX] | Events, probabilities, RVs, limit theorems | LW: Ch. 1-5 | Martin | |
| October 25 | [PDF] [PPTX] | Sampling & statistical inference, max. likelihood, EM | LW: Ch. 6,7,9 | Martin | |
| October 27 | [PDF] | Hypothesis testing, regression, logistic regression | LW: Ch. 10 | Pauli | |
Chapter III: Ranking principles | |||||
| November 3 | [PDF] & [PDF] | Boolean IR, TF-IDF, IR evaluation | MRS: Ch. 1,2,6,8 | Pauli | |
| November 8 | [PDF] [PPTX] | Probabilistic IR, BM25 | MRS: Ch. 11 | Martin | |
| November 10 | [PDF] [PPTX] | Statistical language models | MRS: Ch. 12 | Martin | |
| November 15 | [PDF] [PPTX] | Relevance feedback, XML-IR | MRS: Ch. 9,10; BY: Ch. 5,13 | Martin | |
Chapter IV: Link analysis | |||||
| November 17 | [PDF] | PageRank | MRS: Ch. 21 | Pauli | 1st short test |
| November 22 | [PDF] [PPTX] | HITS, topic-specific & personalized link analysis | MRS: Ch. 21; see also lecture slides |
Martin | |
| November 24 | [PDF] [PPTX] | Spam detection, distributed link analysis, social search | see lecture slides | Martin | |
Chapter V: Indexing & searching | |||||
| November 29 | [PDF] [PPTX] | Inverted lists, merging vs. hashing | MRS: Ch. 4,5; BY: Ch. 9; BCC: Ch. 5 | Martin | |
| December 1 | [PDF] [PPTX] | Index compression, top-k query processing | MRS: Ch. 5; BY: Ch. 9; BCC: Ch. 6 | Martin | |
| December 6 | [PDF] [PPTX] | Top-k ct'd, open-source search engines, efficient similarity search & hashing, LSH | BCC: Ch. 5; see also lecture slides |
Martin | |
Chapter VI: Information extraction | |||||
| December 8 | [PDF] [PPTX] | Similarity search ct'd, IE overview & motivation | Martin | ||
| December 13 | [PDF] [PPTX] | NLP basics, rule- and learning-based extraction, HMMs | see lecture slides | Martin | |
| December 15 | [PDF] [PPTX] | Entity reconciliation, knowledge base construction, Open-IE | see lecture slides | Martin | |
Chapter VII: Frequent itemsets and association rules | |||||
| December 20 | [PDF] | Frequent itemsets & association rules | ZM: Ch. 6 | Pauli | 2nd short test |
| December 22 | [PDF] [PPTX] | Apriori, association rule mining, quality measures | ZM: Ch. 6 | Martin | |
No lectures from Dec. 23 - Jan. 6 | |||||
Chapter VIII: Clustering | |||||
| January 10 | [PDF] | Representation clustering | ZM: Ch. 16, 17; TSK: Ch. 8 | Pauli | |
| January 12 | [PDF] | Hierarchical clustering and co-clustering | Pauli | ||
Chapter IX: Latent topics and dimensionality reduction | |||||
| January 17 | [PDF] | Matrix factorizations | ZM: Ch. 8; TSK: App. B; MRS: Ch. 18; Extra reading: GL | Pauli | |
| January 19 | [PDF] | Matrix factorizations & Latent topic models | Pauli | ||
| January 24 | [PDF] | Latent topic models & Dimensionality reduction | ZM: Ch. 6, 8; | Pauli | |
Chapter X: Classification | |||||
| January 26 | [PDF] | Decision trees | ZM: Ch. 24, 26, 28, 29; TSK: Ch. 4, 5.3 - 5.6 | Pauli | |
| January 31 | [PDF] | Naive Bayes classification | ZM: Ch. 26; TSK: Ch. 5.3 | Pauli | 3rd short test |
| February 2 | [PDF] | Support vector machines | ZM: Ch. 5, 28; TSK: Ch. 5.5; B: Ch. 7.1; | Pauli | |
Chapter XI: Selected topics in DM | |||||
| February 7 | [PDF (part 1)][PDF (part 2)] | Ensemble methods & Data Mining Outro | ZM: Ch. 29; TSK: Ch. 5.6; B: Ch. 14.2-3; | Pauli | |
| February 9 | Wrap up & summary | Pauli & Martin | room changed: E2.5 (Math building) HS2 | ||
Final Exam, February 21 | |||||