Catalog Description: This course offers an introduction to optimization models and their applications, ranging from machine learning and statistics to decision-making and control, with emphasis on numerically tractable problems, such as linear or constrained least-squares optimization.

Units: 4

Related Areas:

• Control, Intelligent Systems, and Robotics (CIR)

Prerequisites: MATH 54 or consent of instructor.

Credit Restrictions: Students will receive no credit for EECS 227AT after taking EECS 127 or Electrical Engineering 127/227AT.

Formats:
Fall: 3.0 hours of lecture and 1.0 hours of discussion per week
Spring: 3.0 hours of lecture and 1.0 hours of discussion per week

Grading Basis: letter

Final Exam Status: Written final exam conducted during the scheduled final exam period

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Class Schedule (Spring 2025):
EECS 127/227AT – TuTh 12:30-13:59, Lewis 100 – Thomas A Courtade

Class Notes
*To enroll in this class, select the lecture and the 999 (placeholder) discussion section. Assignment to the actual sections will be managed by teaching staff.*

Class Schedule (Fall 2025):
EECS 127/227AT – TuTh 09:30-10:59, Stanley 105 – Somayeh Sojoudi

Class Notes
*To enroll in this class, select the lecture and the 999 (placeholder) discussion section. Assignment to the actual sections will be managed by teaching staff.*

Links:

• CS enrollment policies
• EE enrollment policies
• HKN course maps