Nonlinear Time Series Analysis and Manifold Learning Laboratory
Give students the experience of an end to end experience in modern data driven science from problem approach to paper submission
The successful student will participate in a group project and successfully contribute to the completion of an original research paper all the way to submission of a manuscript to a peer reviewed journal and a preprint repository.
The successful student will have performed part of the analysis successfully and his/her work will be checked by other class attendees. The goal is for the students to identify suitable datasets from real life datasets for analysis, Execute and encounter problems such as incomplete data, imperfect processing, data clean up, data integration and compatibility issues encountered in real world datasets. The successful student will acquire skills on how to solve real life difficulties in data science projects.
The goal of this course is to select a group project and take it to completion among the students of the class.
The course will encompass a complete end to end project from Data selection and posing the scientific questions to submission of a complete manuscript to a peer reviewed journal as well as to a preprint server.
The purpose of the project is to teach the students how to apply the techniques that they learned in the previous term in a real life analysis problem. The scope of the class is broader as it also aims to impart instruction on how to choose a scientific problem and the data that will allow answering such question. In addition students will learn best practices for scientific narratives in addition to data driven problem solving .
Students who have not taken A111 will most likely not be prepared to take this class because the relevant material is rarely offered in all but maybe 2 or 3 places in the world.
1. Data selection
2. Scientific question formulation
3. Data suitability assessment
4. Data properties
5. Causal inference and network structure
6. Evidence accumulation
7. Scientific outcomes
8. Manuscript crafting
9. Submission process
10. Scientific integrity and standards of rigor
100% class participation and contribution to the group paper.
Required pass in first theoretical portion of this course, A111 Nonlinear Time series Analysis and Manifold Learning.
Prior deep knowledge of Taken’s theorem-based methods is an absolute prerequisite.
Analysis of Observed Chaotic Data, Henry DI Abarbanel; Springer
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control 2nd Edition, by Steven L. Brunton and J. Nathan Kutz
Offered twice yearly.
Follow-on course from A111 (required)