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|CSCI 1420 - Machine Learning|
We explore the theory and practice of statistical machine learning, focusing on computational methods for supervised and unsupervised data analysis. Specific topics include Bayesian and maximum likelihood parameter estimation, regularization and sparsity-promoting priors, kernel methods, the expectation maximization algorithm, and models for data with temporal or hierarchical structure. Applications to regression, categorization, clustering, and dimensionality reduction problems are illustrated by examples from vision, language, bioinformatics, and information retrieval.
Comfort with basic Multivariable Calculus is recommended.
1.000 Credit hours
1.000 Lecture hours
Levels: Graduate, Undergraduate
Schedule Types: Primary Meeting
Computer Science Department