Foundations of Machine Learning

Catalog description

Concepts of machine learning algorithms for supervised and unsupervised learning tasks. Linear models, decision trees, nearest neighbor, Gaussian mixture models, support vector machines, neural networks, gradient boosting models, Bayesian inferencing, interpretability of machine learning, dimensionality reduction and clustering. Assignments and class projects in a high-level programming language (Python) and cloud-computing platform.