Models of Sequence Data

Instructor: Assistant professor Alexey Zaytsev

Office hours: On request


Location: Online


Co-Instructor: Rodrigo Rivera-Castro

Office hours: On request


📜 Course Description

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.

🗝 Enrollment



Topic List


Test / Quiz

Students will have at the end of the class, either an in-class quiz or a take-home test. They are multiple-choice and are solved online on Canvas. The objective of these quizzes is to solidify the knowledge acquired during the class. The number of quizzes equals to the number of sessions.


Students must write a report of 8 pages, double-column, including plots and bibliography, describing their work and results. This report must be of high quality and comparable to a publication at a small venue for machine learning research. The objective is to submit the reports for publication to machine learning conferences.

As part of the report, students must prepare and submit a small five to eight-minute video presenting their results. Students must do a peer-review and grade the work of their peers.

Team Project

At the beginning of the term, the instructor presents a set of projects to students. Students will form groups of three to four participants. They will then work on selected project.

Projects can consist of reproduction studies, novel work, or analytical tasks. Students must do a peer-review and grade the work of their peers.