Multi Modal Data Fusion (OPEN UNI)

Are you interested in AI, machine learning, and playing with data? Want to extend your knowledge and skills to process and combine information from multiple sources?

Discover the background theory, principles, and techniques behind integrating information from multiple sensors and data sources in the Multi-Modal Data Fusion course. This comprehensive course introduces a statistical framework, exploring concepts like data pre-processing, Bayesian inference, and machine learning approaches for data fusion. Through real-world examples spanning diverse applications, you'll learn how to apply advanced algorithms and models to solve complex challenges.

Whether you're an aspiring researcher or a professional in engineering, AI, machine learning, or data science, this course equips you with the tools to leverage data fusion for impactful solutions.

More detailed, 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