ECE Focus Area: Big Data/Data Mining/Machine Learning (CSE)

Big Data Mining

Overview

Big Data/Data Mining/Machine Learning concerns the analyses of enormous data sets and the extraction of meaning or useful information from them using computer algorithms and/or software tools. Data mining can be used to predict behavior and future trends allowing business to make knowledge-driven decisions.

Big Data/Data Mining/Machine Learning tasks include data summarization, clustering, classification, prediction, and dependency analysis. Big Data/Data Mining/Machine Learning relies heavily on algorithms and statistical methods to uncover patterns and create models of the data.

Big Data/Data Mining/Machine Learning can benefit a broad spectrum of industries helping them to increase profits by reducing costs and/or raising revenue. Students pursuing this FA could literally work in any organization that stores data and is interested in putting that data to good use.

Students interested in this FA are encouraged to consider the course suggestions listed below when completing their Plan of Study Form.

Computer Science and Engineering Requirements Suggested Options

Theory Elective
(Select one)

ECE:5450 Machine Learning (Same as: IGPI:5450)
CS:5430 Machine Learning

5000-Level ECE Elective
(Select one)
ECE:5330 Graph Algorithms and Combinatorial Optimization (Same as: IGPI:5331)
ECE:5320 High Performance Computer Architecture (Same as: CS:5610)
ECE:5995 Contemporary Topics in ECE
ECE:5550 Internet of Things
4000-level or above CS Elective
(Select one)
CS:4400 Database Systems
CS:4420 Artificial Intelligence
CS:4440 Web Mining
CS:4980 Topics in Computer Science II (VARIES BY SEMESTER - Not all sections may be acceptable)
CS:4720 Optimization Techniques (Same as: MATH:4820)
ECE Elective
(Select one)
All 5000-level ECE electives listed above, and
ECE:3400 Linear System II
ECE:3600 Control Systems
BAIS:6480  Knowledge Discovery 
CS Elective
(Select one)

All 4000-level and above CS electives listed above, and

CS:3700 Elementary Numerical Analysis (Same as: MATH:3800)

Additional Electives
(Select one 3 s.h. & one ≥2 s.h.)

MATH:4040 Matrix Theory
STAT:4143 Intro to Statistical Methods
STAT:4520 Bayesian Statistics
STAT:4580 Data Visualization and Data Technologies
IE:3149 Information Visualization
IE:4172 Big Data Analytics

Any of the above OR course selected in consultation with advisor.

Advising Notes

  • If you have special interest in a particular data mining domain it is recommend that you also take one or two courses that provide background in that domain. Introductory courses in bioinformatics, business, or marketing, are examples of domains where data mining is often applied.

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