Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
Gilbert Strang bridges numerical linear algebra and modern machine learning: singular value decomposition, low-rank approximations, Eckart-Young theorem, randomized linear algebra, and stochastic gradient descent.
