Models of Sequential Data (MA030433)
In this course, we discuss the forefront of modern research in learning from sequence data. The course takes a walk from the basics of sequence processing to modern deep learning approaches. We aim at covering both fundamental and modern advances in this area not commonly discussed in undergraduate or graduate Machine Learning and Deep Learning classes.
Over multiple weeks, we will investigate how researchers and practitioners use these methods and algorithms for analyzing time-series data, text data, or medical sequences.
We explore how experts use these concepts for time series forecasting, failure detection of machines, assessing the similarity of CRISP sequences, and more.
The course aims to bring all students on the same page. They do not require severe background knowledge. The objective is to provide them with both depth and breadth knowledge of the state-of-the-art in sequence modeling.