Machine Learning.

Catalog description

This course equips students with knowledge of existing tools for predictive analytics and foundational concepts in machine learning. The course covers core principles of machine learning and pattern analysis. Topics include maximum likelihood estimation, regression; classification; cross validation; generalization and overfitting; introduction to neural networks; nonparametric estimators; clustering; tree-based methods; autoencoders; kernel methods. Applications in tabular, image, and textual data for supervised and unsupervised learning tasks are covered.