EE269
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EE269 - Signal Processing for Machine Learning
Lecture Slides
Introduction, signal processing and machine learning systems
Discrete signals, change of basis
Discrete Fourier Transform (DFT)
DFT based spectral descriptors
Distance based signal classification, nearest neighbor classifier, Hilbert space
Continuous and Discrete Wavelet Transform
Applications of Wavelets and Short Time Fourier Transform: Signal classification, separation and denoising
Linear systems and eigenvector decomposition
Cepstrum and Mel-frequency Cepstral Coefficients (MFCC)
Bayes classifiers, Bayes risk, signal detection
Stationary signals, autocorrelation, linear and quadratic discriminant analysis
Fisher's linear discriminant, simultaneous diagonalization
Multi-class discriminant, separating hyperplanes, Support Vector Machine (SVM)
Constrained optimization, convex duality, dual SVM
Nonlinear features, kernels, kernel machines
Least-squares regression, autoregressive models
Reproducing Kernel Hilbert Spaces and functional regularization
Adaptive filters, Least Mean Squares algorithm
Neural networks
Deep learning, convolutional networks and spectrograms
Convex optimization for neural networks and overparameterized models
Autoencoders, Robust Principal Component Analysis and nuclear norm
Nonnegative Matrix Factorization, clustering and deep matrix factorizations
Dictionary learning and matching pursuit
Diffusion models for waveform and image generation