Information retrieval

Related lectures (32)

Introduces information retrieval basics, emphasizing document frequency and precision in evaluating retrieval quality.

Covers probabilistic information retrieval, query likelihood models, language modeling, and relevance feedback algorithms.

Covers the implementation of Fagin's algorithm for information retrieval, focusing on efficient document retrieval.

Covers embedding models for document retrieval, latent semantic indexing, SVD, and topic models.

Covers TF-IDF computation, document vectors, cosine similarity, and precision formulas.

Explores challenges in question answering systems and solutions, including fine-tuning and open-domain retrieval.

Explains the evaluation of information retrieval models, including recall, precision, F-Measure, and the precision/recall tradeoff.

Introduces a new search feature allowing users to search within people's profiles.

Covers indexing techniques, distributed retrieval algorithms, and challenges in large-scale web indexing.

Explores practical relevance feedback in probabilistic retrieval and query optimization.

Covers Latent Semantic Indexing, word embeddings, and the skipgram model with negative sampling.

Covers the presentation of 4 projects in the course and related logistics.

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