The course covers advanced topics in machine learning, that were not covered in the introductory course. 

Among the topics are:

1. Advanced generalization bounds, based on stability arguments and Rademacher complexity.

2. Topics in graphical models and structured prediction. These are methods used for predicting complex labels, such as image segmentation and semantic analysis of sentences.

3. Selected topics in deep learning, with a focus on unsupervised learning.