Machine Learning for Signal Processing

Second semester 2018

Andrés Marrugo, PhD Universidad Tecnológica de Bolívar

Aims and Scope

Signal Processing deals with the extraction of information from signals of various kinds. This process has two distinct aspects: characterization, and categorization. Traditionally, signal characterization has been performed with mathematically-driven transforms and operations, whereas categorization and classification are operations associated with the use of statistical tools.

Machine learning uses statistical techniques to design algorithms that give computer systems the ability to learn about the state of the world directly from data. In the context of Computer Science, to learn can be explicitly defined as to improve performance on a specific task progressively, without being explicitly programmed. An increasingly popular trend has been to develop and apply machine learning algorithms to both aspects of signal processing.

In this course, we discuss the use of machine learning techniques to process signals. We cover a variety of topics, from data-driven approaches for characterization of signals such as audio including speech, images and video, and machine learning methods for a variety of speech and image processing problems.

In this course, the student will obtain proficiency in:

Useful Resources

Tutorials, review materials

MATLAB reference


This is a new course, this website will be updated as we go along.

Lecture 1: Introduction

We will be discussing the main aspects and motivation for using ML techniques in Signal Processing.

Lecture 2: Linear Algebra Refresher

We will be reviewing the fundamentals of Linear Algebra.

Assignment 1

A summary of Linear Algebra exercises. Due date: 2018-09-14.

Lecture 3: Linear Algebra Refresher II

We will be reviewing the fundamentals of Linear Algebra.

Lecture 4: Signal Representations

We will be discussing the representation of signals, especially the DFT.

Lecture 5: Eigenfeatures

We will take a look at finding data-dependent bases.

Lecture 6: Sparse Representations in Image Processing - Invited

Lecture 7: Face Detection

Assignment 2

A summary of Linear Algebra exercises. Due date: 2018-11-03

Lecture 8: Compressed Sensing - Invited

Lecture 9: Independent Component Analysis

Lecture 10: Clustering

Lecture 11: Expectation Maximization

Lecture 12: Regression and Prediction

Lecture 13: Sparse and Overcomplete Representations

Final exam