Foundations of Machine Learning 2

This course aims to continue the introduction to machine learning building on CSCI3151. The course covers ensemble methods for classification, deep neural networks and their application to structured data, images, and sequences, deep generative models, deep language models, few-shot learning, dimensionality reduction methods, active learning, semi-supervised learning, and Bayesian networks. The course focuses on the underlying mathematical and statistical principles that underpin those methods and how understanding these principles leads to the successful application of machine learning to address practical problems. Students will use standard machine learning libraries to apply algorithms to various datasets.