Explores Association Rule Mining, including support, confidence, single- and multi-dimensional rules, and the Apriori algorithm.
Explores Association Rule Mining, emphasizing Frequent Itemsets and Alternative Measures of Interest, including the FP-Growth algorithm and performance comparison.
Explores knowledge inference for graphs, discussing label propagation, optimization objectives, and probabilistic behavior.
Introduces the basics of information retrieval, covering document representation, query expansion, and TF-IDF for document ranking.
Covers Probabilistic Information Retrieval, modeling relevance as a probability, query expansion, and automatic thesaurus generation.
