EECE 3468 — Analysis of Random Phenomena in Electrical and Computer Engineering
4 semester hoursUndergraduateLectureusually offered: fall, spring, summertypical days: T/WBostonTraditional
Studies analytical concepts and methods for solving engineering problems involving randomness. Uses applications such as data analytics, artificial intelligence, signal processing, computer networks, electronic devices, and sensing systems as guiding examples. Begins with the basic theory of probability and random variables; then develops the concepts of random vectors, sequences, and processes with their statistical description. Introduces basic statistics, regression analysis, parameter estimation, and hypothesis testing, including the use of computer simulations. Defines the concepts of correlation, covariance, and power density spectra. Uses these concepts to discuss noise, uncertainty, and other random phenomena, which is covered in the context of electrical and computer engineering applications.
Prerequisites
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Summer B 2023 | 1 | 22 | 40 | 55% | 18.0 |
| Fall 2023 | 1 | 16 | 30 | 53% | 14.0 |
| Spring 2024 | 1 | 53 | 54 | 98% | 1.0 |
| Summer A 2024 | 1 | 18 | 40 | 45% | 22.0 |
| Summer B 2024 | 1 | 22 | 40 | 55% | 18.0 |
| Fall 2024 | 1 | 31 | 45 | 69% | 14.0 |
| Spring 2025 | 2 | 69 | 75 | 92% | 3.0 |
| Summer A 2025 | 1 | 15 | 40 | 38% | 25.0 |
| Summer B 2025 | 1 | 31 | 40 | 78% | 9.0 |
| Fall 2025 | 1 | 33 | 49 | 67% | 16.0 |
| Spring 2026 | 2 | 72 | 80 | 90% | 4.0 |
Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.
Meeting times
Share of recent sections by weekday: M 39% · T 71% · W 57% · Th 39% · F 29%
Common patterns: TF (29% of sections), MTWR (28% of sections), W (18% of sections), T (14% of sections), MWR (10% of sections) — in patterns, R means Thursday
Professors
Fall
- Mahdi Imani (100% of students) · reviews
Spring
- Iman Salama (73% of students) · reviews
- David Brady (16% of students) · reviews
- Purnima Makris (11% of students) · reviews
Summer A
- Vinay Ingle (100% of students) · reviews
Summer B
- Masoud Salehi (59% of students) · reviews
- Mohammad Mahdi Tajdini (41% of students) · reviews
Percentages are each professor's average share of the season's enrolled students in recent terms.
Unlocks
EECE 5554, EECE 5582, EECE 5612, EECE 5614, EECE 5626, EECE 5644, EECE 5645, EECE 5665, EECE 5666, EECE 7336, ME 5554
Courses that list EECE 3468 in their prerequisites.
Links
Official catalog (EECE course descriptions) · Student reviews on RateMyHusky · All EECE courses · Plan it at numap.app