Course

CS-423: Distributed information systems

Lectures in this course (143)

Covers Probabilistic Information Retrieval, modeling relevance as a probability, query expansion, and automatic thesaurus generation.

Indexing for Information Retrieval

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

Latent Semantic Indexing

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

Information Retrieval Indexing: Part 1

Explores text retrieval systems, inverted files, addressing granularity, and access structures in information retrieval.

Information Retrieval: Fagin's Algorithm

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

Optimization in Machine Learning

Explores optimization techniques, word embeddings, and recommendation systems in machine learning.

Information Retrieval Indexing: Part 2

Explores constructing an inverted file for information retrieval indexing and the map-reduce programming model.

Distributed Information Retrieval

Explores centralized and distributed information retrieval, including Fagin's Algorithm for efficient document identification.

Query Expansion: Methods and Algorithms

Explores query expansion methods, user relevance feedback, Rocchio algorithm, and practical considerations in expanding queries.

Matrix Factorization: Optimization and Evaluation

Explores matrix factorization optimization, evaluation methods, and challenges in recommendation systems.

Indexing and Distributed Retrieval

Explores indexing techniques, inverted files, map-reduce algorithms, and top-k document retrieval methods in text retrieval systems.

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, a technique for mapping documents into a concept space for retrieval and classification.

Probabilistic Information Retrieval

Covers Probabilistic Information Retrieval, including Query Likelihood Model, Language Modeling, and smoothing techniques for non-occurring terms.

Word Embeddings: Lab Session

Covers the implementation of a basic search engine using word embeddings and cosine similarity.

Text Retrieval: Document Ranking

Covers text retrieval tasks with document ranking and re-ranking, using a large corpus for evaluation.

Embedding Models: Concepts and Retrieval

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

Latent Semantic Indexing

Covers Latent Semantic Indexing, a method to improve information retrieval by mapping documents and queries into a lower-dimensional concept space.

Projects Presentation & Logistics

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

Word Embeddings: Models and Learning

Explores word embeddings, context importance, and learning algorithms for creating new representations.