EECE 5612 — Statistical Inference: An Introduction for Engineers and Data Analysts

4 semester hoursGraduateLectureusually offered: springtypical days: M/WBostonOnlinePortland, MaineLive CastTraditionalVideo Streaming

Introduces fundamentals of statistical inference and data analysis through concepts of detection, estimation, and related signal processing algorithms. Addresses topics of hypothesis testing, Bayesian principles, multiple hypotheses and composite hypothesis testing, test power and uniformly powerful tests, likelihood functions, sufficient statistics, optimal estimation, bounds on the estimator variance, minimum variance linear estimation, prediction and regression, interval estimation, and confidence. Extraction of useful information from noisy observations and informed decision making are at the core of multiple disciplines ranging from traditional communications and sensor array processing to biomedical data analysis, pattern recognition and machine learning, security and defense, and financial engineering. Lectures are supported by illustrative examples, hands-on exercises, and numerical implementations grounded in real-world examples.

Prerequisites

• one of:
  • EECE 2520 (min D-) and (
    • one of:
      • EECE 3468 (min D-)
      • MATH 3081 (min D-)
    ) or DS 5020 (min C-)
  • graduate program admission

Offering history

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 91% · T 0% · W 91% · Th 0% · F 0%

Common patterns: MW (91% of sections), async (9% of sections) — in patterns, R means Thursday

Professors

Spring

• Milica Stojanovic (100% of students) · reviews

Percentages are each professor's average share of the season's enrolled students in recent terms.

Links

Official catalog (EECE course descriptions) · Student reviews on RateMyHusky · All EECE courses · Plan it at numap.app