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 and presentations. Students are expected to have a working knowledge of linear algebra, probability theory, and programming skills to carry an implementation of a final project (preferably in MATLAB, but all languages are welcome).
4 hours
Undergraduate degree and courses in linear algebra and probability theory.
Title | Section | CRN | Type | Hours | Times | Days | Location | Instructor |
---|---|---|---|---|---|---|---|---|
Practical Statistical Learning | PSO | 70683 | E3 | 4 | - | Feng Liang |