Machine Learning Engineering

Major: Machine Learning Engineering
Degree Awarded: Master of Science in Machine Learning Engineering (MSMLE)
Calendar Type: Quarter
Total Credit Hours: 45.0
Co-op Option: None
Classification of Instructional Programs (CIP) code: 54.0903
Standard Occupational Classification (SOC) code: 15-1132

About the Program

The MS in Machine Learning is designed to provide students with a strong academic background in machine learning and prepare them for a career as a machine learning engineer or similar position. Using a curriculum based on core machine learning topics, aligned mathematical theory, and signal processing, this graduate program provides a solid mathematical and theoretical understanding of how machine learning algorithms are designed, implemented, and applied to practical problems. Students will gain the ability to implement machine learning systems using standard programming languages, software frameworks, and systems both as an individual and as a member of a development team.
 

Admission Requirements

Applicants must satisfy general requirements for graduate admission including a minimum 3.0 GPA (on a 4.0 scale) for the last two years of undergraduate studies, as well as for any subsequent graduate work. Students will be required to hold a BS in electrical engineering, computer engineering, or computer science; or a bachelor’s degree in an aligned area (e.g. statistics, neuroscience, etc.) in addition to an appropriate technical background which will be reviewed during the admissions process.

Full-time applicants are encouraged to take the GRE exam. Students who do not hold a degree from a US institution must take the TOEFL or IELTS exam within two years of application submission.

Degree Requirements

Core Courses12.0
Machine Learning & Artificial Intelligence
Applied Machine Learning Engineering
Pattern Recognition
Probability & Random Variables
Aligned Mathematical Theory6.0
Choose 2 courses
Random Process & Spectral Analysis
Detection & Estimation Theory
Optimization Methods for Engineering Design
Information Theory and Coding
Linear Algebra & Matrix Analysis
Applied Probability and Statistics I
Applications3.0
Choose 1 course
Cell & Tissue Image Analysis
Multimedia Forensics and Security
Bioinformatics
Statistical Analysis of Genomics
Machine Listening and Music IR
Signal Processing3.0
Choose 1 course
Fundamentals of Deterministic Digital Signal Processing
Fundamentals of Computer Vision
Fundamentals of Image Processing
Engineering Electives9.0
Choose any 3 graduate-level courses from the College of Engineering
Transformational Electives6.0
Choose 2 elective courses that promote the development of leadership, communication, and ethics
Technical Writing
Theories of Communication and Persuasion
Culture, Society & Education in Comparative Perspective
Globalization and Educational Change
Mastery (Thesis and Non-Thesis Option) *6.0
Master's Thesis
Total Credits45.0

Sample Plan of Study

Thesis Option

First Year
FallCreditsWinterCreditsSpringCreditsSummerCredits
ECE 6873.0ECE 6123.0ECE 6103.0VACATION
ECES 5213.0Aligned Mathematical Theory courses6.0Applications course3.0 
Signal Processing course3.0 Engineering elective3.0 
 9 9 9 0
Second Year
FallCreditsWinterCredits  
ECE 8983.0ECE 8983.0  
Engineering elective3.0Engineering elective3.0  
Transformational elective3.0Transformational elective3.0  
 9 9  
Total Credits 45

Non-Thesis Option
 

First Year
FallCreditsWinterCreditsSpringCreditsSummerCredits
ECE 6873.0ECE 6123.0ECE 6103.0VACATION
ECES 5213.0Aligned Mathematical Theory courses6.0Applications course3.0 
Signal Processing course3.0 Engineering elective3.0 
 9 9 9 0
Second Year
FallCreditsWinterCredits  
Aligned Mathematical Theory, Applications, or Signal Processing3.0Aligned Mathematical Theory, Applications, or Signal Processing3.0  
Engineering elective3.0Engineering elective3.0  
Transformational elective3.0Transformational elective3.0  
 9 9  
Total Credits 45
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