Multi Modal Data Fusion (OPEN UNI)

This course will provide a comprehensive introduction to the concepts and ideas of multi-sensor and multi-modal data fusion. We will be concentrated on defining general statistical framework for multi-modal data processing. Using this framework, we will show concepts of common representation and data alignment, Bayesian inference and parameter estimation, sequential Bayesian inference, and machine learning and pattern recognition approaches to data fusion as well as specific models and algorithms in each aforementioned category. Furthermore, the course will illustrate many real-life examples taken from a diverse range of applications to show how they can be benefitted from data fusion approaches.

The course will discuss the following topics:
1. Introduction
2. Sensors and architectures
3. Common representation
4. Data alignment
5. Bayesian inference and parameter estimation
6. Sequential Bayesian inference
7. Bayesian decision theory and ensemble learning
8. Recent advances and applications