Machine learning is a powerful new way to build signal processing models and systems using data rather than physics. This introductory course covers the key ideas, algorithms, and implementations of both classical and modern methods. Topics include supervised and unsupervised learning, optimization, linear regression, logistic regression, support vector machines, deep neural networks, clustering, data mining. A course highlight is a hands-on team project competition using real-world data.

• Time: Tuesdays/Thursdays 10:50am to 12:05pm
• Location: 1064 Duncan Hall
• Instructor:  Prof. Richard Baraniuk
  Duncan Hall 2028
  Office hours: By appointment
• Co-Instructor:  Jeff Lievense
  Zoomland
  Office hours: TBA
• Syllabus
• Prerequisites: Open to students at all levels who are comfortable with linear algebra + coding in Python (ideally), R, MATLAB
• Course Website:  Piazza Course Management Site (It is mandatory that you use this site; all official announcements will be made there) + Gradescope
• Course Email: elec378TA at gmail.com Use this email to contact the course staff (but communication via Piazza is preferred)