Signal Analysis with Machine Learning
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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