Statistical machine learning

This unit covers the methods and practice of statistical machine learning for modern data analysis problems. You will take a deep look at the procedure of learning from data with particular focus placed on how to effectively learn model parameters and methods to guard against overfitting. Topics covered will include stochastic gradient descent, deep neural networks with dropout, convolutional neural networks for image recognition, and text mining and generation with recurrent neural networks. All computing will be conducted using open source software. Introductory machine learning methods such as linear models, decision trees, random forests, and hierarchical clustering, are assumed.