Applied Machine Learning

Catalog description

The course introduces the concepts, algorithms, and applications for modern machine learning approaches. Students will learn the theoretical foundations of supervised learning methods in which labeled training data is available for regression/classification problems. Attention will also be given to unsupervised learning when the label information of the training data is missing. Topics include linear regression, nonlinear regression, support vector machines, neural networks, deep learning, ensemble methods, probabilistic models, clustering, and dimensionality reduction. Emphasis is placed on the mathematical formulation of the machine learning models and their practical applications on various research questions.