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
| Code | Title | Credits |
|---|---|---|
| E C E 204 | Data Science & Engineering 1 | 3 |
| E C E 331 | Introduction to Random Signal Analysis and Statistics (typically offered fall) 2 | 3 |
| E C E/COMP SCI/M E 532 | Matrix Methods in Machine Learning 1 | 3 |
| E C E/COMP SCI/M E 539 | Introduction to Artificial Neural Networks 3 | 3 |
| COMP SCI 564 | Database Management Systems: Design and Implementation 4 | 4 |
| Total Credits | 16 | |
- 1
This course should be taken as a Professional Elective.
- 2
This course fulfills the Probability requirement.
- 3
This course should be taken as a CMPE Elective II.
- 4
This course fulfills the System Software Requirement.
machine learning and data science elective
| Code | Title | Credits |
|---|---|---|
| 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) | ||
| 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 | ||
- 1
This course has additional requisites not required for the B.S. in Computer Engineering.
SAMPLE FOUR-YEAR PLAN
| First Year | |||
|---|---|---|---|
| Fall | Credits | Spring | Credits |
| MATH 221 | 5 | MATH 222 | 4 |
| E C E/COMP SCI 252 | 3 | PHYSICS 201 | 5 |
or Communications A | E C E 204 | 3 | |
| CHEM 103 | 4 | Communications A or | 3 |
| Liberal Studies Elective | 3 | ||
| 15 | 15 | ||
| Second Year | |||
| Fall | Credits | Spring | Credits |
| E C E 203 | 3 | MATH/COMP SCI 240 | 3 |
| E C E 210 | 2 | E C E 219 | 2 |
| E C E/COMP SCI 352 | 3 | E C E 230 | 4 |
| MATH 234 | 4 | E C E 270 | 1 |
| PHYSICS 202 | 5 | COMP SCI 300 | 3 |
| Liberal Studies Elective | 3 | ||
| 17 | 16 | ||
| Third Year | |||
| Fall | Credits | Spring | Credits |
| E C E 353 | 3 | E C E 315 | 1 |
| E C E 340 | 3 | E C E 551 | 3 |
| E C E 331 | 3 | E C E 220 | 3 |
| E C E/COMP SCI 354 | 3 | Circuits Elective | 3 |
| COMP SCI 400 | 3 | INTEREGR 397 | 3 |
| Liberal Studies Elective | 3 | ||
| 15 | 16 | ||
| Fourth Year | |||
| Fall | Credits | Spring | Credits |
| E C E/COMP SCI/M E 532 | 3 | COMP SCI 564 | 4 |
| E C E 453, 454, or 554 | 4 | E C E/COMP SCI/M E 539 | 3 |
| Computer Engineering or Profesional Elective | 3 | Liberal Studies Elective | 3 |
| Liberal Studies Elective | 3 | Machine Learning and Data Science Elective | 3 |
| 13 | 13 | ||
| Total Credits 120 | |||