ELEC 301 – Signals, Systems, and Learning

This course deals with signals, systems, and transforms, from their theoretical mathematical foundations, to practical implementation in circuits and computer algorithms, to machine learning algorithms that convert signals into inferences.

The first half of the course follows the classical "physics" based approach, while the second half follows the more modern "machine learning" approach. In both halves, linear algebra plays a starring role. As a core ECE and data science course, ELEC 301 bridges between the introductory ELEC 241/2 and more advanced courses like ELEC 302, 303, 430, 431, 437, 439, etc. in electrical and computer engineering and the broader area of data science.

  • Location: Zoomland
  • Time: Tuesdays/Thursdays 1:30 to 3pm
  • Instructor:  Richard Baraniuk
    Duncan Hall 2028
    Office hours: By appointment
  • Co-Instructor:  Jeff Lievense
    Email: lievense at rice dot edu
    Office hours: By appointment
  • Prerequisites: ELEC 241: Fundamentals of Electrical Engineering; ELEC 242: Signals, Systems, and Transforms; CAAM 335: Matrix Analysis or equivalent; ELEC 303: Random Signals (co-requisite)
  • Course Email: elec301TA@gmail.com Use this email to contact the course staff with questions, comments, etc.
  • Course Website:  Piazza Course Management Site (It is mandatory that you use this site; all official announcements will be made there.)