Planned Topics

     • Introduction and prerequisites refresher:  Course goals, linear algebra and probability

     • Representation and interpretation of signals:  Human perception of signals, sampling, quantization, the frequency domain, image and sound representations

      • 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

     • More feature analysis and dimensionality reduction: Independent Component Analysis (ICA), Non-Negative Matrix Factorization (NMF), Kernel PCA, Manifold embedding methods, 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 interlude. K-means, Gaussian Mixture Models, Expectation-Maximization algorithm

     • Time series and dynamical models: Classification and similarity, time warping models, Markov models

     • 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

     • Deep Learning. Boltzman Machines, Neural Networks, Convolutional and Recurrent models, etc.

     • Missing data techniques and tracking

     • Special Topic: Computer Vision

     • Special Topic: Machine listening and Music Information Retrieval

     • Special Topic: Speech

     • Special Topic: Compressive Sensing

Lectures

Intro and Linear Algebra [PDF, Slides]

Probability and Stats [PDF, Slides]

DSP Primer [PDF, Slides]

Perception and Features [PDF, Slides]

Principal Component Analysis [PDF, Slides]

ICA and NMF [PDF, Slides]

KPCA and Manifold Methods [PDF, Slides]

Detection and Matched Filters [PDF, Slides]

Decision theory & classifiers [PDF, Slides]

Nonlinear classifiers [PDF, Slides]

Classification bits and pieces [PDF, Slides]

Clustering [PDF, Slides]

DTW and HMMs [PDF, Slides]

Missing data & dynamical models [PDF, Slides]

Arrays & source separation [PDF, Slides]

Underconstrained separation [PDF, Slides]

Matrix Factorizations and beyond [PDF, Slides]

Compressive Sensing and Sparsity[PDF, Slides]

Deep Learning[PDF, Slides]

Deep Learning II[PDF, Slides]

Deep Learning II[PDF, Slides]

Privacy-Preserving Learning and Requests[PDF, Slides]

Problem Sets

Problem Set 1


Problem Set 2


Problem Set 3


Problem Set 4