The first part of the course discusses key concepts and introduces students to well-established techniques and algorithms in Information Retrieval: the vector space model, relevance, PageRank, indexing, language-models and learning to rank.

The final part of the course takes the initial concepts from classic IR and applies them to multimedia information and examines the special requirements of this data.

Knowledge
- Learn the concept of information relevance.
- Analyze Web and multimedia data.
- Learn how to rank information by relevance.
- Understand evaluation protocols.

Know-how
- Implement information retrieval models.
- Ability to adapt and improve components of a search engine.
- Deploy search engines with large-scale datasets.
- Design evaluation protocols and evaluate search engines.

Soft-Skills
- Select the right IR techniques for particular problems.
- Design information retrieval systems.
- Ability to do critical thinking about retrieval results.

1. Introduction

2. Web Search

3. Basic retrieval models

4. Link-based ranking

5. Indexing

6. Probabilistic retrieval

7. Language models

8. User feedback and query expansion

9. Evaluation

10. Learning-to-rank

11. Distributed retrieval

12. Image retrieval

13. High-dimensional data indexing

- C. D. Manning, P. Raghavan and H. Schütze, "Introduction to Information Retrieval", Cambridge University Press, 2008. http://www-nlp.stanford.edu/IR-book/

- S. Büttcher. C. L. A. Clarke, G. V. Cormack, "Information Retrieval: Implementing and Evaluating Search Engines", The MIT Press, 2010. http://www.ir.uwaterloo.ca/book/

- Rick Szeliski , "Computer Vision: Algorithms and Applications". Springer, 2013. http://szeliski.org/Book/