Precision and recall

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Page 3 of 4

Introduces the basics of Information Retrieval, covering indexing, weighting schemes, cosine similarity, and query evaluation.

Explores hydraulic turbine modeling, stability, and historical development, emphasizing the selection criteria for Francis turbines.

Covers linear models for classification, logistic regression training, evaluation metrics, and decision boundaries.

Explores the Vector Space model, Bag of Words, tf-idf, cosine similarity, Okapi BM25, and Precision and Recall in Information Retrieval.

Explores the importance of precision in movement and the impact of speed and damping on structural stress.

Covers the organization of the geometric stereotomy course, focusing on muqarnas, stone block modeling, and pavilion design.

Explores thresholding, ROC curves, regression evaluation, naive methods, and model selection in machine learning.

Covers the systematization and convergence of 2D finite element models.

Covers linear models, binary classification, logistic regression, and model evaluation metrics.

Explores model assessment metrics, selection techniques, bias-variance tradeoff, and handling skewed data distributions in machine learning.