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 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
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 43
E C E/​COMP SCI/​I SY E  524 Introduction to Optimization3
Total Credits15
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 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 should be taken as an Advanced Elective and meets the advanced math auxiliary condition.

MACHINE LEARNING AND DATA SCIENCE ELECTIVE

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)
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 B.S. in Electrical Engineering.

SAMPLE FOUR-YEAR PLAN

First Year
FallCreditsSpringCredits
MATH 2215PHYSICS 2015
CHEM 1034MATH 2224
E C E 2102Communication A or Liberal Studies Elective3
Liberal Studies Elective or Communication A3E C E/​COMP SCI  2523
 14 15
Second Year
FallCreditsSpringCredits
PHYSICS 2025COMP SCI 3003
MATH 2344E C E 2192
E C E 2033E C E 2304
E C E 2043E C E 3303
 E C E 2701
 15 13
Third Year
FallCreditsSpringCredits
E C E/​PHYSICS  2353ECE Advanced Elective3
E C E 3313INTEREGR 3973
E C E 3403Liberal Studies Elective3
E C E 2711EE Advanced Lab (3XX)1
E C E 2203Liberal Studies Elective3
E C E/​COMP SCI  3523MATH 3203
 16 16
Fourth Year
FallCreditsSpringCredits
E C E/​COMP SCI/​I SY E  5243ECE Advanced Elective (4XX)3
E C E 3702ECE Advanced Elective (4XX)3
ECE Advanced Elective3Machine Learning and Data Science Elective3
ECE Advanced Elective4E C E/​COMP SCI/​M E  5323
Liberal Studies Elective3Liberal Studies Elective3
EE Advanced Lab (3XX)1 
 16 15
Total Credits 120