Explores evaluation protocols in machine learning, including recall, precision, accuracy, and specificity, with real-world examples like COVID-19 testing.
Explains the evaluation of information retrieval models, including recall, precision, F-Measure, and the precision/recall tradeoff.
Covers probabilistic retrieval models, evaluation metrics, query likelihood, user relevance feedback, and query expansion.
Introduces the basics of information retrieval, covering text-based and Boolean retrieval, vector space retrieval, and similarity computation.
Covers the basics of information retrieval using vector space models and practical exercises on relevance feedback and posting list scanning.
Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.
Covers Probabilistic Information Retrieval, modeling relevance as a probability, query expansion, and automatic thesaurus generation.
Introduces the basics of information retrieval, covering document representation, query expansion, and TF-IDF for document ranking.
Discusses the evaluation of binary classifiers, including recall, sensitivity, specificity, ROC curves, and performance measures.
