ECE 314 - Probability in Engineering Lab

Semesters Offered

TitleRubricSectionCRNTypeHoursTimesDaysLocationInstructor
Probability in Engineering LabECE314A66503LAB0900 - 1050 R  2022 ECE Building Bruce Hajek
Probability in Engineering LabECE314B66505LAB1100 - 1250 R  2022 ECE Building Bruce Hajek
Probability in Engineering LabECE314C68868LAB1300 - 1450 R  2022 ECE Building Bruce Hajek

Official Description

Designed to be taken concurrently with ECE 313, Probability in Engineering Systems, to strengthen the students' understanding of the concepts in ECE 313 and their applications, through computer simulation and computation using the Python programming language. Topics include sequential hypothesis testing, parameter estimation, confidence intervals, Bloom filters, min hashing, load balancing, inference for Markov chains, PageRank algorithm, vector Gaussian distribution, contagion in networks, principle component method and linear regression for data analysis, investment portfolio analysis. Course Information: Prerequisite: Concurrent enrollment in ECE 313 or one of: ECE 313, IE 300, STAT 410.

Course Director

Lab Equipment

Access to Engineering Work Stations during class hours for Python programming. Many students can an choose to use their own laptops instead. Python software is open source and easily installed.

Lab Software

Python software with basic modules for numerical and statistical modeling and analysis.

Topical Prerequisites

Basic probability theory, as provided by concurrent enrollment in ECE 313.

Texts

Reading is provided within an iPython notebook for each lab, with reference to the ECE 313 notes.

Required, Elective, or Selected Elective

Elective. Counts as a laboratory course for EE majors.

Course Goals

Strengthen the students' understanding of the concepts in probability and engineering applications. This involves a mixture of review and use of concepts in ECE 313, and introduction of real applications that require concepts related to, but beyond those, in ECE 313.

Instructional Objectives

Have students how to solve problems involving uncertainty through reasoning and computer programming.

Enhance student effectiveness in using a scientific programming language such as Python to solve problems.

Last updated

2/4/2017by Bruce Hajek