Instructor : Cem Subakan

This class is about learning to build machine learning algorithms for signals. Different from a standard machine learning class, we will have a little bit more of an EE flavor to things. That is, we will often work with sequential data such as speech and audio, and other signals. We will give the necessary background to be able to propose and carry out a research or applied project in the domain of machine learning for signal processing. In the end, our goal is to teach how to fish (for MLSP projects)!

This class is influenced by classes with the same title in UIUC, CMU, and Indiana University.

Week 1 : Linear Algebra Refresher slides

Matrix multiplication

Index, Matrix, Tensor notations

Eigenvalues, Eigenvectors

Building the reflexes to avoid for loops

Signal representations

Tensors, Funky Tensor Mathematics

Linear Algebraic Matrix Decompositions

Week 2 : Probability Refresher

Probability Calculus, Bayes Rule

Continuous and Discrete Random Variables

Multidimensional Random Variables

Probabilistic Graphical Model Conventions

Week 3: Signal Processing Refresher

Continuous and Discrete Signals

Sampling, Analog to Digital Conversion

Fourier Transform, Discrete-Cosine Transform, Short Time Fourier Transform

Filtering

Mechnanics of Convolution in Time Domain, Convolution as a Matrix Multiply

Week 4: Machine Learning 1: Decompositions

Linear Regression

Linear Regression connections with Fourier Transform

Dimensionality Reduction, PCA and its variants, ICA, NMF

Week 5: Machine Learning 2: Non-linear Dimensionality Reduction

Kernel PCA

Multidimensional Scaling

Manifold Learning Methods

ISOMAP

Locally Linear Embeddings

Laplacian Eigenmaps

TSNE

Week 6: Machine Learning 3: Classification

Generative Classification

Discriminative Classification

Perceptron Algorithm

Logistic Regression

Kernel Logistic Regression

Neural Network Classifier

Week 7: Deep Learning Primer

Feedforward Networks

Skip Connections

Convolutional Layers

Recurrent Layers

Attention Layers

Gradient Descent and variants

Week 8 (Invited Lecture ??)

Week 9: Machine Learning 4: Clustering

Kmeans clustering

Mixture Models

Expectation Maximization, Iterative Conditional Modes

Spectral Clustering

Hierarchical Clustering

Week 10: Time Series Models

Dynamic Time Warping

Hidden Markov Models, Forward-Backward Algorithm

EM for HMMs

Viterbi Decoding

HMM Variants (Mixture of HMMs, Factorial HMMs,…)

Week 11: Graph Signal Processing / Graph ML

Signals as Graphs

Graph Fourier Transform

Graph Methods for Signal Processing

Graph Convolutions

Graph Neural Networks

Week 12: Speech / Audio

Automatic Speech Recognition (ASR)

Text-to-Speech

Speech Separation / Enhancement

Interpretability in the Audio Domain

Text-Audio Multi-Modal Representations

Week 13: Project Presentations

There will be 3 homeworks (45%), labs (10%) and a project (45%) that will be carried out by teams of 2-3 students. It is preferable that the students propose the project, but we will propose several projects ideas also.