Course dates – MT – week beginning Monday 15th October 2018 – for Year 1 students
- Provide an overview of classical signal processing concepts and tools
- Provide an overview of filters and stochastic models
- Provide an overview of signal representations via transforms and dictionaries
- Present the concepts and practice of the new field of signal processing on graphs
- LTI systems, convolution, time-/frequency-domain analysis, filters
- Sampling, digital filters, z-transform, frequency response of digital filters
- Design of digital filters
- Spectral estimation, discrete Fourier transform, stochastic models
- Time-frequency representation, Fourier and wavelet transforms
- Dictionary learning
- Introduction to signal processing on graphs
- Representation and learning of signals on graphs
- Application I: Learning graphs from data
- Application II: Deep learning on graphs
- Linear algebra
- Probability and statistics
- MATLAB and Python
- An introduction to the analysis and processing of signals. Macmillan, 1989.
- Oppenhein and Shafer. Digital signal processing. Prentice Hall, 1975.
- Proakis and Manolakis. Digital signal processing: Principles, algorithms and applications. Prentice Hall, 2007.
- Introduction to signal processing. Prentice Hall, 1996.
- Vetterli et al. Foundations of Signal Processing. Cambridge University Press, 2014.
- Kovačević et al. Fourier and wavelet signal processing. Online at http://www.fourierandwavelets.org
- Rubinstein et al., “Dictionaries for sparse representation modeling,” Proceedings of the IEEE, 2010.
- Shuman et al., "The emerging field of signal processing on graphs," IEEE Signal Processing Magazine, 2013.
- Dong et al., “Learning graphs from data,” arXiv, 2018.
- Bronstein et al., “Geometric deep learning,” IEEE Signal Processing Magazine, 2017.
- Coding methods and testing on real world data