Machine Learning

3 units

This course is the second course of a two course sequence on machine learning. It assumes a fundamental understanding of the machine learning foundations (both theoretical and practical) and introduces probabilistic machine learning approaches in-depth as well as advanced topics at the frontier of the field. Topics to be covered include discriminative and generative probabilistic models, kernel methods (e.g., support vector machines, Gaussian processes), neural networks (deep learning), unsupervised learning techniques, and practical machine learning (e.g., feature engineering, dimensionality reduction, model comparison).