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What are the most essential mathematical concepts one has to be familiar with for succeeding in the field of Image/Signal Processing and Machine Learning. I am somewhat familiar with Tensors, Scale-Space, Filtering, Transforms, PCA/SVD etc. But the basis of these topics is somewhat lacking and therefore I want to build my mathematical concepts. Can you please suggest some easy to understand resources for learning relevant mathematical topics ?

Thanks.

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  • $\begingroup$ Oppenheim and Schaffer is a popular introductory book. It's what I used and I would not hesitate to recommend it. I've never read Lyons, but I've heard people speak highly of it. $\endgroup$ – AnonSubmitter85 May 23 '14 at 3:59
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1) time to frequency domain transforms (Fourier transforms in particular) 2) Sampling theorem (Nyquist rates and things related to that) 3) Matrix mathematics 4) Filtering (Transfer functions, impulse response etc)

I would also recommend to start with a small image processing project using software like MATLAB.

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