Course Summary
This course will introduce you to the fundamentals, implementation, and applications of Digital Signal Processing (DSP) techniques as applied to practical, real-world problems.
The course will extend your knowledge acquired in Signals and Systems 1 (EEET2369). You will learn how to formulate practical engineering problems in terms of signal processing tasks and how to solve these problems using signal processing methods.
The laboratory assignments included in this course were designed in consultation with local industry to give you practical knowledge of current industry practice in signal processing, machine learning and data analysis.
Particular topics to be investigated include:
- Correlation
- Random signals
- Signals in noise
- Matched filters
- Algorithms machine learning and supervised classification
- Algorithms for machine learning and unsupervised clustering
- Basic Neural Networks
- Numerical techniques for Deep Learning and Convolutional Neural Networks
- Deep transfer learning
- Autoencoders
If you are enrolled in this course as a component of your Bachelor Honours Program, your overall mark will contribute to the calculation of the Weighted Average Mark (WAM).