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: DH1046
• Time: Tuesdays/Thursdays 1 to 215pm
• Instructor:  Dr. Vishwa Saragadam
  Duncan Hall 2028
  Office hours: TBA
• Co-Instructor:  Jeff Lievense
  Zoomland
  Office hours: TBA
• 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 Website:  Piazza Course Management Site (It is mandatory that you use this site; all official announcements will be made there) + Gradescope
• Course Email: elec301TA at gmail.com Use this email to contact the course staff (but communication via Piazza is preferred)