EE269
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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