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

Also Offered As: EECS 227AT

Related Areas:

• Control, Intelligent Systems, and Robotics (CIR)

Prerequisites: Mathematics 54 or equivalent or consent of instructor.

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: Student Option

Final Exam Status: Yes

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Class Schedule (Spring 2026):
EECS 127/227AT – TuTh 14:00-15:29, Stanley 105 – Gireeja Vishnu Ranade

Class Schedule (Fall 2026):
EECS 127/227AT – MoWe 12:30-13:59, The Gateway Building 1210 – Somayeh Sojoudi

Class Notes
Phase 1 and 2 seats are open to EECS Grad and EECS MEng students. 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.*

Links:

• EECS Course Archives (CalNet authentication required)
• CS enrollment policies
• EE enrollment policies
• HKN course maps