Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) (M-I-T)

Lecture 27: Backpropagation: Find Partial Derivatives (M-I-T)

Lecture 8: Norms of Vectors and Matrices (M-I-T)

Lecture 2: Multiplying and Factoring Matrices (M-I-T)

Lecture 9: Four Ways to Solve Least Squares Problems (M-I-T)

Lecture 30: Completing a Rank-One Matrix, Circulants! (M-I-T)

Lecture 31: Eigenvectors of Circulant Matrices: Fourier Matrix (M-I-T)

Lecture 32: ImageNet is a Convolutional Neural Network (CNN), The Convolution Rule (M-I-T)

Lecture 33: Neural Nets and the Learning Function (M-I-T)

Lecture 34: Distance Matrices, Procrustes Problem (M-I-T)