Discusses the evaluation of binary classifiers, including recall, sensitivity, specificity, ROC curves, and performance measures.
Covers the introduction to Quantum Chaos, classical chaos, sensitivity to initial conditions, ergodicity, and Lyapunov exponents.
Delves into NLP evaluation, covering gold standards, precision, recall, and statistical significance.
Introduces machine learning basics, including data collection, model evaluation, and feature normalization.
Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
