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CS-423: Distributed information systems
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Lectures in this course (143)
Information Retrieval Basics: Boolean and Vector Space Models
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Introduces Boolean and Vector Space models for information retrieval, covering syntax, similarity computation, term frequency, and query weights.
Information Retrieval Basics: Document Frequency and Precision
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Introduces information retrieval basics, emphasizing document frequency and precision in evaluating retrieval quality.
Probabilistic Information Retrieval
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Covers probabilistic information retrieval, query likelihood models, language modeling, and relevance feedback algorithms.
Probabilistic Retrieval: Practical Relevance Feedback
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Explores practical relevance feedback in probabilistic retrieval and query optimization.
Information Retrieval Indexing: Part 1
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Explores text retrieval systems, inverted files, addressing granularity, and access structures in information retrieval.
Distributed Information Systems: Overview and Models
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Covers Distributed Information Systems, key tasks, methods, projects, evaluation, and exam support.
Anchor Text Indexing and Page Ranking
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Explores anchor text indexing, PageRank, and link-based ranking using a random walker model.
Optimization in Machine Learning
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Explores optimization techniques, word embeddings, and recommendation systems in machine learning.
Matrix Factorization: Optimization and Evaluation
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Explores matrix factorization optimization, evaluation methods, and challenges in recommendation systems.
Text Retrieval: Document Ranking
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Covers text retrieval tasks with document ranking and re-ranking, using a large corpus for evaluation.
Projects Presentation & Logistics
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Covers the presentation of 4 projects in the course and related logistics.
Knowledge Representation: Semantics and Data Structures
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Explores knowledge representation, data structures, semantics, and the challenges of searching for data on the web.
Information Extraction: Methods and Applications
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Explores methods for information extraction, including traditional and embedding-based approaches, supervised learning, distant supervision, and taxonomy induction.
Knowledge Inference
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Explores knowledge inference, embedding techniques, and schema matching in data integration.
Information Retrieval: Indexing and Retrieval
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Covers indexing techniques, distributed retrieval algorithms, and challenges in large-scale web indexing.
Association Rules Mining
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Covers association rules mining, focusing on Apriori and FP-growth algorithms to find frequent itemsets and extract rules efficiently.
Information Retrieval Basics
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Introduces the basics of information retrieval, covering text-based and Boolean retrieval, vector space retrieval, and similarity computation.
Probabilistic Retrieval Models
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Covers probabilistic retrieval models, evaluation metrics, query likelihood, user relevance feedback, and query expansion.
Latent Semantic Indexing
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Covers Latent Semantic Indexing, word embeddings, and the skipgram model with negative sampling.
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