The Machine Learning and Data Science option in Electrical Engineering prepares students for a career in electrical 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 18 of the elective credits within the 120-credit Electrical Engineering BS 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.
Requirements
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 |
| Choose one: | 3 | |
| Linear Algebra and Differential Equations 3 | ||
| Elementary Matrix and Linear Algebra 3 | ||
| Linear Algebra 3 | ||
| E C E/COMP SCI/M E 532 | Matrix Methods in Machine Learning 4 | 3 |
| E C E/COMP SCI/I SY E 524 | Introduction to Optimization | 3 |
| Total Credits | 15 | |
- 1
This course can be taken as a Professional Elective.
- 2
This course fulfills the Probability requirement.
- 3
This course can be taken as a Professional Elective and meets the advanced math auxiliary condition. MATH 375 Topics in Multi-Variable Calculus and Linear Algebra and MATH 376 Topics in Multi-Variable Calculus and Differential Equations taken in sequence will fulfill the requirement for MATH 340 Elementary Matrix and Linear Algebra.
- 4
This course can be taken as an Advanced Elective and meets the advanced math auxiliary condition.
Machine Learning and Data Science Elective
| Code | Title | Credits |
|---|---|---|
| Choose one as an Advanced or Professional Elective: | 3-4 | |
| Digital Signal Processing (typically offered fall) | ||
| Image Processing (typically offered fall) | ||
| Introduction to Artificial Neural Networks | ||
| Probability and Information Theory in Machine Learning (typically offered fall) | ||
| Ethics of Data for Engineers | ||
| Linear Optimization | ||
| Introduction to Artificial Intelligence | ||
| Database Management Systems: Design and Implementation 1 | ||
| Medical Image Analysis 1 | ||
| Introduction to Bioinformatics | ||
| Introduction to Algorithms 1 | ||
| 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 BS in Electrical Engineering.
Four-Year Plan
Sample Four-Year Plan
| First Year | |||
|---|---|---|---|
| Fall | Credits | Spring | Credits |
| MATH 221 | 5 | PHYSICS 201 | 5 |
| CHEM 103 | 4 | MATH 222 | 4 |
| E C E 210 | 2 | Communications A or Liberal Studies Elective | 3 |
| Liberal Studies Elective or Communications A | 3 | E C E/COMP SCI 252 | 3 |
| 14 | 15 | ||
| Second Year | |||
| Fall | Credits | Spring | Credits |
| PHYSICS 202 | 5 | E C E 222 | 4 |
| MATH 234 | 4 | E C E 230 | 4 |
| E C E 203 | 3 | E C E 270 | 1 |
| E C E 204 | 3 | E C E 330 | 3 |
| Free Elective | 1 | Liberal Studies Elective | 3 |
| 16 | 15 | ||
| Third Year | |||
| Fall | Credits | Spring | Credits |
| COMP SCI 300 | 3 | ECE Advanced Elective | 3 |
| E C E 331 | 3 | E C E/PHYSICS 235 | 3 |
| E C E 340 | 3 | EE Advanced Lab (3XX) | 1 |
| E C E 271 | 1 | INTEREGR 397 | 3 |
| E C E/COMP SCI 352 | 3 | Liberal Studies Elective | 3 |
| MATH 320 | 3 | ||
| 13 | 16 | ||
| Fourth Year | |||
| Fall | Credits | Spring | Credits |
| E C E/COMP SCI/I SY E 524 | 3 | ECE Advanced Elective (4XX) | 3 |
| E C E 370 | 2 | ECE Advanced Elective (4XX) | 3 |
| ECE Advanced Elective | 3 | Machine Learning and Data Science Elective | 3 |
| ECE Advanced Elective | 4 | E C E/COMP SCI/M E 532 | 3 |
| Liberal Studies Elective | 3 | Liberal Studies Elective | 3 |
| EE Advanced Lab (3XX) | 1 | ||
| 16 | 15 | ||
| Total Credits 120 | |||