Course material

Note: Up-to-date teaching material and course information will become available on our MS Teams page during the semester. Please see home page for instructions on how to subscribe to the course Team.

Planned topics

Introduction and prerequisites refresher: Course goals, linear algebra, tensor representations, and probability
Representation and interpretation of signals: Human perception of signals, sampling, quantization, the frequency domain, image and sound representations
Linear feature analysis and discovery: Useful fixed transforms (DCT, etc), adaptive transforms (KLT/PCA/EM-PCA/online-PCA), feature extraction from familiar signals (audio, video), eigenfaces
Advanced feature analysis and dimensionality reduction: Independent Component Analysis (ICA), Non-Negative Matrix Factorization (NMF), Kernel PCA, Manifold embedding methods (ISOMAP, LLE, LE), random projections
Detection and classification: Matched filters, template matching, object detection, similarity measures, face detection, speech detection. Linear classifiers, linear discriminant analysis
Classification: Non-linear classifiers, neural nets, kernels, generative models, non-parametric methods. Real-world applications of classification models
Clustering: K-means, Gaussian Mixture Models, Expectation-Maximization algorithm
Time series and dynamical models: Classification and similarity with time series, Dynamic Time Warping, Hidden Markov Models, Time-Series Regression, Kalman Filtering, tracking and prediction
Deep Learning: Boltzman and Hopfield Networks, Generative Models, Deep Learning techniques, time-series deep models (Convolutional, Recurrent, Attention-Based).
Graph Signal Procesing: Signals as graphs, Graph Convolutions, Graph methods on time-series data, Graph Neural Networks.
Mixed signals: Array processing, beamforming, independent component analysis, MIMO/SIMO models, under-constrained separation, spectral factorizations
Matrix factorizations and bag-of-features models: Non-negative Matrix Factorization and Probabilistic Latent Semantic Decompositions, bag models, Convolutive decompositions, Tensor Decompositions
Missing data techniques: Linear Prediction models, global statistics model methods, patch-based missing data methods
Compressive Sensing and sparsity, Random Projections, high-dimensional data issues
Area Topics: Speech, computer Vision, MIR, ...

Machine Learning Resources

Textbook: Pattern Recognition, by Theodoridis and Koutroumbas (UIUC access only)
Online book: The Elements of Statistical Learning, by Hastie, Tibshirani and Friedman
Online book: Information Theory, Inference and Learning Algorithms, by McKay
Online book: Bayesian Reasoning and Machine Learning, By Barber
Lectures: Machine Learning @ videolectures.net
Data Sets: The UCI Machine Learning Repository

Signal Processing Resources

Book: Signal Processing for Communications, by Prandoni and Vetterli
Links: Julius Smith’s Online Publications on Audio and DSP
Links: Compressive Sensing Page @ Rice

Math References

Mathematics for Machine Learning, by Deisenroth, Faisal, and Ong

Matrix Differential Calculus, by Magnus and Neudecker