Probabilistic Retrieval

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Related lectures (32)

Page 3 of 4

Information Retrieval Basics: Boolean and Vector Space Models

Introduces Boolean and Vector Space models for information retrieval, covering syntax, similarity computation, term frequency, and query weights.

Indexing for Information Retrieval

Explores indexing techniques, inverted files, map-reduce models, and trie usage for efficient information retrieval.

Document Retrieval and Classification

Covers document retrieval, classification, sentiment analysis, and topic detection using TF-IDF matrices and contextualized word vectors like BERT.

Embedding Models: Concepts and Retrieval

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

Information Retrieval Indexing: Latent Semantic Indexing

Explores Latent Semantic Indexing in Information Retrieval, discussing algorithms, challenges in Vector Space Retrieval, and concept-focused retrieval methods.

Latent Semantic Indexing: Concepts and Applications

Explores latent semantic indexing, vocabulary construction, document matrix creation, query transformation, and document retrieval using cosine similarity.

Pretraining: Transformers & Models

Explores pretraining models like BERT, T5, and GPT, discussing their training objectives and applications in natural language processing.

System Modeling Languages

Explores the significance of System Modeling Languages like OPM, SysML, and Modelica in modern Systems Engineering.

Word Embeddings: Modeling Word Context and Similarity

Covers word embeddings, modeling word context and similarity in a low-dimensional space.

Text Data Processing: Basics & Techniques

Introduces the basics of text data processing, covering document retrieval, classification, sentiment analysis, and topic detection.