The Machine Learning and Data Science option in Computer Engineering prepares students for a career in computer engineering with an emphasis on machine learning and data science. The purpose of this option is to provide guidance and recognition for students pursuing this career path. The option uses 19 of the elective credits within the 120-credit Computer Engineering B.S. degree program to focus on the mathematics, tools, and practices associated with machine learning and data science in engineering. Students selecting this option must submit an option declaration form to the dean’s office in Engineering Hall.

Machine Learning and data science required courses

E C E 204 Data Science & Engineering 13
E C E 331 Introduction to Random Signal Analysis and Statistics (typically offered fall) 23
E C E/​COMP SCI/​M E  532 Matrix Methods in Machine Learning 13
E C E/​COMP SCI/​M E  539 Introduction to Artificial Neural Networks 33
COMP SCI 564 Database Management Systems: Design and Implementation 44
Total Credits16

This course should be taken as a Professional Elective.


This course fulfills the Probability requirement.


This course should be taken as a CMPE Elective II.


This course fulfills the System Software Requirement.

machine learning and data science elective

Choose one as an Advanced, Professional, or Free Elective:3-4
Digital Signal Processing (typically offered fall) 1
Introduction to Optimization 1
Image Processing (typically offered fall) 1
Probability and Information Theory in Machine Learning (typically offered fall)
Web Programming
Linear Optimization 1
Introduction to Artificial Intelligence
Medical Image Analysis 1
Introduction to Bioinformatics
Introduction to Algorithms
Fundamentals of Industrial Data Analytics
Machine Learning in Action for Industrial Engineers
Data and Algorithms: Ethics and Policy
Introduction to Stochastic Processes 1
An Introduction to Brownian Motion and Stochastic Calculus 1
Introduction to Computational Materials Science and Engineering 1
Applied Categorical Data Analysis 1
Statistical Experimental Design 1
Applied Multivariate Analysis 1
Financial Statistics 1

This course has additional requisites not required for the B.S. in Computer Engineering.


First Year
MATH 2215MATH 2224
or Communications A
E C E 2043
CHEM 1034Communications A or3
Liberal Studies Elective3
 15 15
Second Year
E C E 2033MATH/​COMP SCI  2403
E C E 2102E C E 2192
E C E/​COMP SCI  3523E C E 2304
MATH 2344E C E 2701
 Liberal Studies Elective3
 17 16
Third Year
E C E 3533E C E 3151
E C E 3403E C E 5513
E C E 3313E C E 2203
E C E/​COMP SCI  3543Circuits Elective3
COMP SCI 4003INTEREGR 397 (was EPD 397)3
 Liberal Studies Elective3
 15 16
Fourth Year
E C E/​COMP SCI/​M E  5323COMP SCI 5644
E C E 453, 454, or 5544E C E/​COMP SCI/​M E  5393
Computer Engineering or Profesional Elective3Liberal Studies Elective3
Liberal Studies Elective3Machine Learning and Data Science Elective3
 13 13
Total Credits 120