Information Retrieval
Formalia
- Zielgruppe
- Angewandte Informatik Master mit 3+1 Wochenstunden und 6 Kreditpunkten
- Komedia Master mit 3+1 Wochenstunden und 6 Kreditpunkten
- ISE Master mit 3+1 Wochenstunden und 6 Kreditpunkten
Termine
Vorlesung
Tag | Zeit | Ort |
| Donnerstag | 12:30 - 15:00 | LC/137 |
Übungen
Tag | Zeit | Ort | Betreuer |
| Donnerstag | 15:00 - 15:45 | LC/137 | Dr. Ahmet Aker |
Prüfungstermine
Klausur
Tag | Zeit | Ort |
| 03. 09. 2019 | 10:00 - 12:00 |
Beschreibung
Course Topics
- Extended Models: Predicate logic, ontologies
- Search User Interfaces: Query Specification, Presentation of Search Results, Query Reformulation, Supporting the Search Process, Designing the Search Process
- IR tasks:Learning to Rank, Classification, Clustering
- Implementation of IR Systems: Data structures, algorithms
- Deep Learning in IR
Vorlesungsmaterial
Books
- Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze, Introduction to Information Retrieval. Cambridge University Press, 2008.
- Bruce Croft, Donald Metzler, Trevor Strohman: Search Engines: Information Retrieval in Practice. Addison-Wesley, 2009.
- R. Baeza-Yates, B. Ribeiro-Neto: Modern Information Retrieval. Addison Wesley.
- Marti A. Hearst: Search User Interfaces. Cambridge University Press. (2009)
- Some further IR books and course notes are linked here.
Literature on specific topics
- Models
- N. Fuhr (2014): Bridging Information Retrieval and Database
- Clustering
- N. Fuhr; M. Lechtenfeld; B. Stein; T. Gollub (2011): The Optimum Clustering Framework: Implementing the Cluster Hypothesis
Example exam questions
Slides
For copyright reasons, the slides are accessible from within the universitry network only!
Topics in parentheses are not relevant for the exam!
- Lehrangebot des Fachgebiets
- Introduction
- Models
- Models based on predicate logic
- Learning and classification
- Clustering
- User Interfaces
- Deep Learning in IR:
- Refresher: Concept of Machine Learning
- Artificial Neural Networks (ANNs) and Keras
- Relevant Jypter Notebooks for the above lectures
- Some Practical Concepts
- Relevant Jypter Notebooks for the above lecture
- RNN and LSTM
- Relevant Jypter Notebooks for the above lecture
- More on LSTM
- More on LSTM
- CNN and GAN
- CNN and GAN
- Relevant Jypter Notebooks for the above lecture
- Practical Considerations 2 and Recent Advances
- Practical Considerations 2 and Recent Advances
Übungen
Ziel der Übungen ist es, dass Studierenden lernen, die in der Vorlesung behandelten Konzepte auf konkrete Beispiele anzuwenden.
Die Übungen finden alle zwei Wochen statt. Die genauen Termine werden auf der Webseite angekündigt.
Übungsblätter
- Sheet 1 (for 02.05.19)
- Sheet 2 (for 09.05.19)
- Sheet 3 (for 16.05.19)
- Sheet 4 (for 06.06.19)
- Deep Learning Install Instructions
- Sheet 5 (for 13.06.19)
- Sheet 6 (for 04.07.19)
- Sheet 7 (for 11.07.19)