Linear Algebra in Data Analysis and Machine Learning

EEE 361 treats linear algebra as the working language of modern data analysis, framing techniques like SVD, PCA, least squares, and the backprop chain rule as one continuous story about decomposing matrices to extract structure from data. You'll work through Strang alongside ISLP, with five homeworks that push you from regression and resampling into SVMs, trees, clustering, and small neural nets, plus a midterm and final to consolidate the theory. It sits at the bridge between the math you already know from Math 255/230 and the ML and signal-processing electives that follow, so the payoff is being able to read a learning algorithm and immediately see the matrix factorization underneath.