Question

Is signal processing important in machine learning?

Answer 1

Answer:

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Explanation:

We see that machine learning can do what signal processing can, but has inherently higher complexity, with the benefit of being generalizable to different problems. The signal processing algorithms are optimal for the job in terms of complexity, but are specific to the particular problems they solve.

Signal processing is a branch of electrical engineering used to model and analyse analog and digital data representations of physical events. All the technology we use today and even rely on in our everyday lives (computers, radios, videos, mobile phones) is enabled by signal processing.

Signal processing is essential for the use of X-rays, MRIs and CT scans, allowing medical images to be analyzed and deciphered by complex data processing techniques. Signals are used in finance, to send messages about and interpret financial data. This aids decision-making in trading and building stock portfolios.

Machine Learning takes vast amounts of data (hence Big Data) to learn from the patterns. It creates self-learning algorithms so that machines can learn from themselves.