Machine Learning

          for Signal Processing

Fall 2017

Course Description

Today we see an increasing need for machines that can understand complex real-world signals, such as speech, images, movies, music, biological and mechanical readings, etc.  In this course we will cover the fundamentals of machine learning and signal processing as they pertain to this goal, as well as exciting recent developments.


We will learn how to decompose, analyze, classify, detect and consolidate signals, and examine various commonplace operations such as finding faces from camera feeds, organizing personal music collections, designing speech dialog systems and understanding movie content.  The course will consist of lectures and student projects/presentations.  


Textbook

“Pattern Recognition, 4th ed.” by Theodoridis and Koutroumbas (UIUC access).


Grading

Grades will be determined based on attendance (10%), homework (40%) and a small team final project (50%).

Time:

Room:

Wed/Fri 3:30-4:45PM

Siebel Center 0216

Instructor:

TAs:

Wed 5-6PM


Siebel 0207

TA Hours: