EE269 - Signal Processing for Machine Learning

Lecture Slides

Lecture 1: Introduction, signal processing and machine learning systems

Lecture 2: Discrete signals, change of basis

Lecture 3: Discrete Fourier Transform

Lecture 4: Distance based signal classification, Nearest Neighbor classifier, Hilbert space

Lecture 5: Linear systems, circulant matrices, convolution, Eigenvector decomposition

Lecture 6: Bayes classifiers, Bayes risk, signal detection

Lecture 7: Stationary signals, autocorrelation, Linear and Quadratic Discriminant Analysis

Lecture 8: Fisher's Linear Discriminant, simultaneous diagonalization

Lecture 9: Multi-class Discriminant, Separating Hyperplanes, Support Vector Machines

Lecture 10: Constrained Optimization, Convex Duality, Dual SVM

Lecture 11: Nonlinear features, Kernels, Kernel Machines

Lecture 12: Least Squares Regression, Autoregressive Models

Lecture 13: Reproducing Kernel Hilbert Spaces, Regularization

Lecture 14: Adaptive Filters, Least Mean Squares Algorithm

Lecture 15: Neural Networks

Lecture 16: Convolutional Nets, Deep Learning

Lecture 17: Wavelets, Discrete Wavelet Transform

Lecture 18: Nonnegative Matrix Factorization, Dictionary Learning