Machine perception is the ability of an embodied device to perceive, comprehend, and reason about the surrounding environment. This course will introduce students to foundational principles of geometric and statistical learning approaches for machine perception. Topics include sensing techniques (vision, motion, audio, touch), probabilistic state estimation, localization and mapping, 3D reconstruction, object detection, and scene understanding algorithms. Students will implement, debug and test machine perception algorithms on different sensory data in Python through labs and hands-on programming assignments. Prerequisite: CS225. One of CS440, CS446, ECE484, or equivalent, is recommended.

  • Time: Wednesday/Friday 12:30-1:45 pm
  • Location: Zoom
  • Discussion: Piazza
  • Assignments Submission: Gradescope (Course Code: JBXJVZ)
  • Online Lectures: The lectures will be live-streamed through Zoom, recorded, and made available on MediaSpace. Please log in with your institution account.
  • Contact: Students should ask all course-related questions on Piazza, where you will also find announcements. For external inquiries, personal matters, or in emergencies, you can email the course instructors (email title starts with [CS498]).