COURSE

DSAA4011

Advanced Machine Learning and Deep Learning

Information Hub / Data Science and Analytics · 4 Credits

In this course, topics include: SVM, kernel methods, ensemble learning, dimensionality reduction, Gaussian Mixture Models, Hidden Markov Models, Topic Models, semi-supervised learning, transfer learning, and domain adaptation. This course extends classical ML and deep learning with a few advanced themes: kernel-based methods; ensemble and representation learning; probabilistic modeling and latent variable methods; learning beyond labels, including semi-, self-, and unsupervised approaches, and out-of-distribution learning. Students will learn to design, analyze, and implement these advanced learning approaches as well as handle non-typical learning scenarios.