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: EECS 16A and EECS 16B, or consent of instructor.

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

Formats:
Fall: 3 hours of lecture and 1 hour of discussion per week
Spring: 3 hours of lecture and 1 hour 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

Phase 1 and 2 seats are open to EECS, CS, and non-EECS COE majors. Remaining seats open during the adjustment period. NO TIME CONFLICTS WITH LECTURE *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.* **Enrollment Permission seats are reserved for internal programs and are not open. Please DO NOT email the instructor or scheduling to request a seat**

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

Class Notes

Phase 1 and 2 seats are open to EECS, CS, and non-EECS COE majors. Remaining seats open during the adjustment period. *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.* **Enrollment Permission seats are reserved for internal programs and are not open. Please DO NOT email the instructor or scheduling to request a seat**

Links:

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