Focuses on analyzing and extracting features from signals using the signal processing toolbox of MATLAB. The signal’s statistical and spectral features will be used as input for machine learning models, ground truth labeling will be explored to create a labeled dataset for supervised learning.
Binary classification models such as, logistic regression, support vector machine, and shallow neural network will be trained to identify good and faulty signals.
Machine learning fundamentals will be briefly discussed. Machine learning modeling and training will be done using the machine learning toolbox’s classification learner app. Signal processing and analytics using MATLAB’s signal analyzer app will be used to analyze and clean the signals.
Highlights
- Definition of signals
- Examples of signals
- Introduction to signal processing and spectral analysis
- Extracting features from signals
- What is Machine Learning (ML)?
- Machine learning tasks and subsets
- Machine learning applications in the modern world
- Signal classification using machine learning demonstration
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