Catalog Description: This course covers the fundamentals of probability and random processes useful in fields such as networks, communication, signal processing, and control. Sample space, events, probability law. Conditional probability. Independence. Random variables. Distribution, density functions. Random vectors. Law of large numbers. Central limit theorem. Estimation and detection. Markov chains.

Units: 4

Related Areas:

• Information, Data, Network, and Communication Sciences (IDNCS)
• Signal Processing (SP)

Prerequisites: COMPSCI 70 preferred but not required; Familiarity with linear algebra.

Credit Restrictions: Students will receive no credit for EECS 126 after completing EE 126.

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

---

Class Schedule (Spring 2025):
EECS 126 – TuTh 14:00-15:29, Lewis 100 – Kannan Ramchandran

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.*

Class Schedule (Fall 2025):
EECS 126 – TuTh 12:30-13:59, Hearst Mining 390 – Preeya Khanna

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