Fundamentals of Statistical Learning II

Catalog description

A second course in statistical learning. Supervised learning models includes regression trees, bagging, and random forests. Model assessment and scoring methods, including cross-validation. Regularization and model tuning. An introduction to neural networks including convolutional and recurrent networks. Unsupervised learning models including Principal Component Analysis. Statistical software will be used, and programming required.