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: Duncan Hall Room 1042
- Time: Tuesdays/Thursdays 1:00 to 2:15pm
- Instructor: Richard Baraniuk
Duncan Hall 2028
Office hours: By appointment
- Teaching Fellow: Jeff Lievense
Email: lievense at rice dot edu
Office hours: TBA
- Prerequisites: ELEC 241: Fundamentals of Electrical Engineering I, ELEC 242: Fundamentals of Electrical Engineering II, 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.)