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 Machine (SVM)
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 and functional regularization
Lecture 14: Adaptive filters, Least Mean Squares algorithm
Lecture 15: Neural networks
Lecture 16: Deep learning, convolutional networks and spectrograms
Lecture 16: Convex optimization for neural networks and overparameterized models
Lecture 17: Wavelets, Discrete Wavelet Transform and Short-Time Fourier Transform
Lecture 18: Autoencoders, Robust Principal Component Analysis and nuclear norm
Lecture 19: Nonnegative Matrix Factorization, clustering and deep matrix factorizations
Lecture 20: Dictionary learning and matching pursuit