Time & Place. Tue/Thu, 11:00-12:15, Stanley Thomas 302.
Instructor. Parisa Kordjamshidi (Office hours: Stanley Thomas 305 B, Tu/Th 12:15-1:30pm).
This course is a graduate level Machine Learning course. However, it is open to a small number of undergraduate students too.
We will cover a number of introductory topics and classic machine learning approaches as well as several advanced topics. The advanced topics that are covered in this course include areas of deep learning, structured output prediction based on generalized linear models, Constrained Conditional Models, Learning and inference with Probabilistic Graphical Models. Moreover, we cover topics on classical feature extraction, dimensionality reduction as well as new techniques for learning representations and semi-supervised learning. The course includes research projects and presentations.
Recommended General Books
Papers and Tutorials