11.1 Signals and systems through an ML lens
You can map convolution, filtering and sampling onto ML operations you already use.
Before:06. Deep Learning
Convolution in digital signal processing and convolution in CNNs are the same operation; sampling theory governs data resolution choices; LTI systems echo linear models — this topic maps ECE vocabulary onto ML's, and the mapping is a genuine advantage over CS-trained peers. It opens the bridge module because the mapping makes everything after it half-familiar. The waste to avoid is keeping the two worlds separate in your head when one set of intuitions serves both.
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Convolution in DSP vs. in CNNs
The convolution of signal processing and the convolution of neural networks are close relatives with a sign convention between them. Seeing the connection makes both easier for anyone who has met either.
Sampling, aliasing and data resolution choices
How often you sample decides what you can represent, and sampling too slowly makes high frequencies masquerade as low ones. The same reasoning applies to choosing the resolution of any data.
LTI systems and linear models
Linear time-invariant systems and linear models share a mathematical structure, which is why the analysis carries across. This is the clearest bridge between the electronics subject and this one.
Fourier intuition for feature design
Thinking about a signal in terms of its frequency content suggests features that a time-domain view hides. This intuition is what makes the next topic productive rather than mechanical.
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