Course overview
This course covers an introduction to signal processing, from its traditional theoretical foundations in Fourier analysis and information theory, through to modern approaches involving deep learning and transformer-based models. It explores how modern deep learning both adheres to and extends the foundational mathematical theory of information, and develops students’ ability with practical signal processing techniques.
- Foundations
- Optimisation
- Applications of Deep Signal Processing
Course learning outcomes
- Describe traditional and modern approaches to signal processing and connect these with the underlying mathematical principles
- Demonstrate an understanding of how to represent and analyse signals using traditional and deep learning approaches
- Demonstrate skills in analysing signals using computer programming of both traditional and modern techniques
- Apply mathematical techniques to solve a variety of problems at an appropriate level of difficulty
- Demonstrate skills in communicating about information theory and deep learning using mathematics
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