Lecture Notes of "Matrix Methods in Data Analysis, Signal Processing, and Machine Learning"

Course : Matrix Methods in Data Analysis, Signal Processing, and Machine Learning

Source : https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/index.htm

Lecture 15: Matrices A(t) Depending on t, Derivative = dA/dt
Lecture 16: Derivatives of Inverse and Singular Values
Lecture 17: Rapidly Decreasing Singular Values
Lecture 18: Counting Parameters in SVD, LU, QR, Saddle Points
Lecture 19: Saddle Points Continued, Maxmin Principle
Lecture 20: Definitions and Inequalities
Lecture 21: Minimizing a Function Step by Step
Lecture 22: Gradient Descent: Downhill to a Minimum
Lecture 23: Accelerating Gradient Descent (Use Momentum)
Lecture 24: Linear Programming and Two-Person Games
Lecture 25: Stochastic Gradient Descent
Lecture 26: Structure of Neural Nets for Deep Learning
Lecture 27: Backpropogation: Find Partial Derivatives
Lecture 30: Completing a Rank One Matrix, Circulants!
Lecture 31: Eigenvectors of Circulant Matrices: Fourier Matrix
Lecture 32: ImageNet is a Convolutional Neural Network (CNN), The Convolution Rule
Lecture 33: Neural Nets and the Learning Function
Lecture 34: Distance Matrices, Procrustes Problem
Lecture 35: Finding Clusters in Graphs