Digital Signal Processing

DSP is where you stop thinking of signals as continuous waveforms on an oscilloscope and start treating them as vectors in a Hilbert space that a computer can actually manipulate — the whole course is built around the idea that sampling, transforms, and filtering are just changes of basis. Expect five problem sets that mix pencil-and-paper derivations (z-transform manipulations, FIR/IIR design from specs, sampling rate conversion) with a midterm, final, and a quiz, all leaning more theoretical than hands-on lab work. It sits downstream of Signals and Systems and is the gateway to communications, control, and anything ML-adjacent that touches audio, images, or sensor data, so the filter-design and random-signal intuition you build here keeps paying off long after the final.