I studied EE Signal processing in Bachelors and Masters, and now work as a machine learning engineer.

In signal processing, you take the signal, define a desirable output and try to create a system to get to that output.

In ML, you typically have the input and the output, and your machine figures out the system depending on the network architecture you provide.

Both are solving the same problem most times, and extensive signal processing knowledge helps a ton in understanding what's happening/what should happen within a network.

And FWIW EE signal processing has most requirements for AI covered in their coursework, and makes good ML engineers minus production level coding.