STAT-627 Statistical Machine Learning (3)

Introduction to statistical concepts, models, and algorithms of machine learning and artificial intelligence (AI). Explores supervised learning for regression and classification, unsupervised learning for clustering and principal components analysis, and related topics such as discriminant analysis, splines, lasso and other shrinkage methods, bootstrap, regression and classification trees, and support vector machines, along with their tuning, diagnostics, and performance evaluation. The course builds the mathematical and algorithmic foundation of AI, focusing on how learning algorithms are trained, evaluated, and optimized. Students explore key AI concepts such as training and prediction, overfitting and underfitting, regularization, hyperparameter tuning, and performance metrics that are essential for developing efficient and interpretable learning systems. Includes review of linear algebra and optimization methods supporting the above topics. Crosslist: STAT-427. Grading: A-F only. Prerequisite: STAT-520 or STAT-615.

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