Course

CS-423: Distributed information systems

Lectures in this course (143)

Explores adjacency lists, locality, similarity, and efficient representation in link indexing.

Document Classification: Transformers and MLPs

Explores transformers and MLPs for document classification, emphasizing their benefits over traditional methods.

Covers the challenges and opportunities of data mining, practical questions, algorithm components, and applications like shopping basket analysis.

Recommender Systems: Part 1

Introduces recommender systems, collaborative filtering, content-based recommendation, similarity metrics, and matrix factorization.

Recommender Systems: Matrix Factorization & Evaluation

Explores matrix factorization techniques for recommender systems, including evaluation metrics like RMSE and NDCG.

Association Rule Mining

Introduces association rule mining, covering support, confidence, Apriori algorithm, and FP-growth for frequent itemset discovery.

Recommender Systems: Basics and Techniques

Covers collaborative filtering and content-based methods for recommender systems, addressing cold start problems and making predictions.

Association Rules: Mining and Algorithms

Explores Association Rule Mining, Apriori algorithm, and rule generation using support and confidence.

Latent semantic indexing: inverted files

Explores term-offset indices in inverted files and relevance feedback solutions.

Recommender Systems: Matrix Factorization

Explores matrix factorization in recommender systems, covering optimization, evaluation metrics, and challenges in scaling.

Association Rules

Explores Association Rule Mining, including support, confidence, single- and multi-dimensional rules, and the Apriori algorithm.

FP-Growth: Itemset Mining

Introduces the FP-Growth algorithm for mining frequent itemsets in transaction datasets.

Association Rules: Mining and Analysis

Explores association rules in data mining, covering measures, techniques, and algorithms for efficient rule extraction.

Semantic Web: Knowledge Graphs

Explores knowledge representation, information extraction, and the Semantic Web vision, emphasizing standardization, mapping, and ontologies in structuring data.

Association Rules: Frequent Itemsets

Explores Association Rule Mining, emphasizing Frequent Itemsets and Alternative Measures of Interest, including the FP-Growth algorithm and performance comparison.

Clustering: K-Means

Covers clustering and the K-means algorithm for partitioning datasets into clusters based on similarity.

Semantic Web: Modeling and Ontologies

Explores the Semantic Web, database schemas, XML data model, and ontologies.

Semantic Web: Ontologies and RDF Modeling

Covers the creation and encoding of ontologies, modeling RDF statements, syntax, and classification.

Advanced Clustering: DBSCAN

Covers Density-based Clustering (DBSCAN) and its algorithm step by step.

Evaluating Clustering Quality

Explores the silhouette coefficient for evaluating cluster quality and other cluster evaluation measures.