Course overview
This course aims to provide students with a deep understanding and practical proficiency in handling and analysing multi-modal datasets. It covers advanced data integration techniques, feature extraction, and machine learning models tailored for multi-modal data. By engaging with real-world case studies, students will apply these techniques to industry-relevant problems, developing the ability to evaluate, adapt, and implement solutions that address complex challenges across various domains. The course emphasises both the theoretical underpinnings and practical applications, ensuring that students are prepared to lead in the development and deployment of multi-modal data analysis solutions.
Course learning outcomes
- Demonstrate a comprehensive understanding of the different types of multi-modal data, including their unique characteristics, challenges, and pre-processing requirements.
- Apply advanced data fusion techniques and machine learning models to integrate multi-modal data, enhancing prediction, classification, and decision-making capabilities.
- Critically evaluate and measure the effectiveness and efficiency of different data integration, analysis, and machine learning techniques on multi-modal datasets.
- Select and adapt appropriate algorithms and models to address specific industry challenges, demonstrating the ability to tailor solutions to diverse application contexts.
- Design and implement a comprehensive solution to a real-world problem involving multi-modal data, showcasing the ability to work collaboratively and present findings effectively.
- Critically assess the ethical considerations and potential biases in the integration and analysis of multi-modal data and propose strategies to mitigate these challenges.