Machine Learning Engineering MSMLE

Major: Machine Learning Engineering
Degree Awarded: Master of Science in Machine Learning Engineering (MSMLE)
Calendar Type: Quarter
Minimum Required Credits: 45.0
Co-op Option: Available for full-time, on-campus master's-level students
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 an 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.

Students are also encouraged to engage in thesis research. The combined thesis and research cannot exceed 9.0 credits. The MS program is organized so that a student may complete the degree requirements in less than 2 years of full-time study or 2-3 years of part-time study.

Students within the Master of Science in Machine Learning Engineering are eligible to take part in the Graduate Co-op Program, which combines classroom coursework with a 6-month, full-time work experience. For more information, visit the Steinbright Career Development Center's website.

Additional Information

For more information about the MS in Machine Learning Engineering program, please visit the Department of Electrical and Computer Engineering website.

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
Theories of Communication and Persuasion
Culture, Society & Education in Comparative Perspective
Education for Global Citizenship, Sustainability, and Social Justice
Mastery (Thesis and Non-Thesis Option) *6.0
Master's Thesis
Total Credits45.0
*

Thesis Option: A minimum of two terms of laboratory-based research that leads to a publicly defended MS thesis. Students will be advised by a faculty member, and when applicable, a representative of industry or government sponsor.

Non-thesis Option: In lieu of research and thesis, students will complete six additional credits of coursework from the Mathematical Theory, Applications, or Signal Processing area.

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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