INFORMATION RETRIEVAL
INFORMATION RETRIEVAL
This course provides a comprehensive exploration of information retrieval (IR) techniques aimed at improving the effectiveness of search and retrieval systems. The course begins with methods such as relevance feedback and query expansion, which refine search queries to improve results, and then looks at fundamental approaches such as probabilistic models and language models tailored to IR.
Subsequent sections cover important topics such as text classification and introduce key algorithms such as Naïve Bayes, k-nearest neighbors (kNN), and linear classifiers. In the final segments, students will explore state-of-the-art classification learning methods and clustering algorithms, focusing on real-world applications and evaluation techniques. By the end of the course, students will gain a solid understanding of both traditional and state-of-the-art IR methods, enabling them to tackle complex retrieval challenges.
- In collaboration with: Marco Fisichella & Franziska Neuhof (Leibniz University Hannover)
- Estimated time: ≈ 11h