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.
- Understanding multi-modal data
- Techniques for multi-modal data analysis
- Industrial applications and real-world practices
Course learning outcomes
- Describe the different types of multi-modal data, including their unique characteristics, quality issues and challenges, and create pre-processing pipelines.
- Apply advanced data fusion techniques and machine learning models to integrate multi-modal data, enhancing prediction, classification, and decision-making capabilities.
- Evaluate the effectiveness and efficiency of different data integration and machine learning techniques on multi-modal datasets.
- Select and adapt appropriate algorithms and models to address industry problems involving multi-modal data and tailor solutions to diverse application contexts.
- Design and implement a solution to a real-world problem involving multi-modal data and present findings effectively.
- Assess ethical considerations and potential biases in the integration and analysis of multi-modal data and propose strategies to mitigate challenges.
Availability
Class details
Adelaide City Campus East
Group 1
Class number 61141
Section FR01
Size 160
Available 160
Class number 61142
Section PR04
Size 40
Available 40
Class number 61143
Section PR03
Size 40
Available 40
Class number 61144
Section PR02
Size 40
Available 40
Class number 61145
Section PR01
Size 40
Available 40
Class number 68639
Section PR05
Size 40
Available 40
Fee calculator
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