Machine LearningOffered this year and every year
How can artificial systems learn from examples and discover information buried in data? We explore the theory and practice of statistical machine learning, focusing on computational methods for supervised and unsupervised learning. Specific topics include empirical risk minimization, probably approximately correct learning, kernel methods, neural networks, maximum likelihood estimation, the expectation maximization algorithm, and principal component analysis. This course also aims to expose students to relevant ethical and societal considerations related to machine learning that may arise in practice.
|Meeting Time:||K hr: TTh 2:30pm-3:50pm|
|Exam Group:||0-MAY-2023 02:00 PM|