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.
