| PRE-REQUISITE | DSAA 2011 OR DSAA 2012 |
|---|---|
| DESCRIPTION | 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. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6620) | Th 09:00AM - 11:50AM | Rm 233, W1 | HUANG, Yuwen | 40 | 8 | 32 | 0 | |
| LA01 (6621) | Fr 03:00PM - 03:50PM | Rm 233, W1 | HUANG, Yuwen | 40 | 8 | 32 | 0 |