Sensing
New!
Active Learning
Lectures by Frankie Zhu
In this five-part series, Dr. Frankie Zhu will:
In this five-part series, Dr. Frankie Zhu will:
- Introduce active learning algorithms and their benefits,
- Explore the key components of these algorithms,
- Discuss how to select models that underpin active learning, and
- Examine various applications and limitations.
- The series will conclude with a presentation by Hans Mertens on a recent advance in using active learning for identifying the dynamics of diverse systems.
Miniseries on Observability Theory
Lectures by Floris van Breugel
In this miniseries we will cover the principles of observability, including analytical tools, empirical tools, example applications, and software demonstrations.
Observability is a tool in control theory that provides a formal framework for quantifying how well-posed an estimation task is given certain sensor measurements and trajectory geometries.
Observability can be used for trajectory design, sensor selection, and data curation in machine learning applications.
In this miniseries we will cover the principles of observability, including analytical tools, empirical tools, example applications, and software demonstrations.
Observability is a tool in control theory that provides a formal framework for quantifying how well-posed an estimation task is given certain sensor measurements and trajectory geometries.
Observability can be used for trajectory design, sensor selection, and data curation in machine learning applications.
