Program Overview
The University of New Mexico is pleased to offer a fully online master of science program in Computer Engineering with a specialization in Applied Machine Learning & Artificial Intelligence Systems Engineering. Courses are offered in eight and 16 week online formats. This graduate program was developed with input from our industrial partners, including Google, Sandia National Laboratories, and many others. Machine Learning and AI systems are expected to have a transformative impact on several aspects of human life, science, and engineering. All students will be required to work one-to-one with an ECE faculty advisor to complete a Machine Learning and AI system project.
Instructors
The courses are taught by our world-renowned computer engineering faculty from the UNM School of Engineering. Professor Ioannis Papapanagiotou, a principal engineer at Google, teaches our AI infrastructure and Cloud Computing courses and has helped us co-design our course on the Introduction on the Internet of Things to support the latest and the next generation of Networks. Professor Christopher Lamb, a cybersecurity scientist with Sandia National Laboratories, teaches Introduction to the Internet of Things and Cybersecurity courses.
The Senior leadership of the ECE Department is directly responsible for guaranteeing the quality of the program.
- Professor Marios Pattichis is the director of ECE online programs and teaches the Problems course, supervising the development of Machine Learning projects, Digital Image Processing, and Optimization Theory. He is a Fellow of the European Alliance of Medical and Biological Engineering and Science and a Senior Member of the National Academy of Inventors.
- Professor Manel Martinez-Ramon teaches the core courses in Machine Learning and Deep Learning. He holds the King Felipe VI Endowed Chair in ECE.
- Dr. Papapanagiotou teaches the Cloud Computing and the AI Infrastructure courses. He is a Technical Director/Principal Engineer at Google responsible for AI in the reliability of Ads and Commerce systems (the highest revenue and traffic systems globally).
- Professor Michael Devetsikiotis teaches the Advanced Networking course. He is an IEEE Fellow.
- Professor Christos Christodoulou offers projects in the Problem courses that are also relevant to the Space Industry. He is an IEEE Fellow and Distinguished Professor of ECE.
Possible Careers
Students who complete the degree will be prepared for engineering positions at national labs, including LANL and Sandia National Labs, as well as in industry and government. Students will be ready to develop and implement Machine Learning and AI systems, and can further pursue PhD Degrees in AI and Machine learning.
Disclosures
All online and distance education is protected by federal regulations and policies. For details on these protections, refer to the following resources: Online Education Regulations, Professional Licensing Disclosures, and Complaint Resolution.
Admissions
Tuition and Fees
Total cost per credit hour is $538.72 and is comprised of: $432.40 base tuition + $106.32 college differential. There is a student technology fee charged each semester. It is $180 in the Fall, $180 in the Spring, and no fee in the Summer. There may be additional costs, depending on the class. Additional costs are often for textbooks, supplemental course materials, and proctoring fees. Graduate students are charged a $42 per semester Graduate and Professional Students Association (GPSA) fee.
Admissions Requirements
The Master of Science in Computer Engineering with a concentration in Applied Machine Learning & Artificial Intelligence Systems Engineering is an accelerated online program that is designed to accommodate students with an interest in computing and the emerging problems associated with machine learning and AI. Students from all STEM majors who have completed undergraduate courses in Probability and Statistics, Advanced Calculus, Linear Algebra, and Programming are encouraged to apply. This program is not subject to application deadlines, and applications may be submitted at any time before the start of the term for which the student is applying. Students must maintain a 3.0 GPA in the required graduate coursework and achieve a grade of B or better in the required core courses. Applicants must have:
- A minimum GPA (grade point average) of 3.0 (or equivalent) in the undergraduate area of study.
- College-level proficiency in English (reading and writing), programming, and mathematics.
- Three (3) letters of recommendation.
- Statement of Purpose/Letter of Intent, which should include AI and Machine Learning project interest.
- Language Skills for International Students: International students must meet all UNM Graduate Admissions Requirements, including tests for English proficiency. For more information regarding international students, please see International Students and Online Education. For international students, the minimum Graduate TOEFL Scores (Paper/Computer) are: 550/213, minimum Graduate IELTS Score: 6.5, or a Duolingo minimum score of 105. Official test results must be sent directly from ETS to the University of New Mexico (code #4845). Please refer to the ETS website for TOEFL testing options and updates.
- The GRE is no longer required for admission to ECE graduate programs.
Application Process
Application Deadlines: Accelerated Online Programs (AOPs), such as M.S. Computer Engineering-Applied Machine Learning and AI Systems Engineering, M.S. Computer Engineering-Internet of Things and M.S. Electrical Engineering-Space Systems are not subject to deadlines. AOP applications may be submitted at any time prior to the start of the term a student is applying for.
When completing the Program of Interest section within the UNM Graduate Admissions Application, use the answers provided below on the following topics:
Are you applying for an online-only program?: Yes
What program are you applying for?: Computer Engineering
What Degree Type are you applying for?: MS
Select your Area of Interest: Applied Machine Learning & Artificial Intelligence Systems Engineering
Degree Requirements
The degree is offered as a Master's Plan III Option (coursework only) program, which consists of:
- A minimum of 31 hours of coursework, which can include between 13 and 16 credit hours of the required courses and 15 to 18 credit hours of electives.
- At least 13 credit hours must be ECE core courses. The remaining courses are free electives.
- A maximum of 6 hours of 400-level ECE courses, and no more than 6 hours of 400-level Non-ECE courses (400-level courses must be approved for graduate credit. ECE 495 cannot be used for graduate credit).
- A maximum of 6 hours in "problems" courses (ECE 551 or ECE 651).
- At least 50% of required coursework must be completed after admission to the graduate program.
- At least one credit hour, but not more than two credit hours, of graduate seminar (ECE 590).
- No more than half of the minimum required coursework hours may be taken with a single faculty member.
- No more than 6 credit hours of coursework can have a grade of C (2.0), C+ (2.33) or CR (grading option selected by student). ECE 590 is excluded from this limitation.
- The CR grading option is not allowed for ECE core courses.
- A student's cumulative GPA cannot be below 3.0. In addition, the GPA for courses presented in their program of studies cannot be below 3.0.
The master of science degree consists of five required courses plus an additional six elective courses. The courses can be taken in any order and can be completed in as little as 18 months.
Look for the courses in the current schedule of classes with a course comment that reads "Limited to managed online program students only." For more information, contact the ECE Grad App Assistance team at eceadmissions@unm.edu or 505.277.2436.
Required Courses (13-16 credit hours):
| Course Number | Course Name | Credit Hours |
| ECE 517 | Machine Learning | 3 |
| ECE 533 | Digital Image Processing | 3 |
| ECE 510 | Deep Learning | 3 |
| ECE 590 | Graduate Seminar | 1 |
| ECE 551 | Problems (1-6) | 3-6 |
Additional Elective Courses (15-18 credit hours):
Select courses from the following list, in consultation with an advisor:
| Course Number | Course Name | Credit Hours |
| ECE 506 | Optimization Theory | 3 |
| ECE 522 | Hardware Software Codesign with FPGAs | 3 |
| ECE 529 | Introduction to Cybersecurity | 3 |
| ECE 530 | Introduction to Cloud Computing | 3 |
| ECE 531 | Introduction to the Internet of Things | 3 |
| ECE 535 | Satellite Communications | 3 |
| ECE 540 | Advanced Networking | 3 |
| ECE 561 | Engineering Electrodynamics | 3 |
| ECE 595 | AI Infrastructure | 3 |
| ECE 595 | Cybersecurity II | 3 |
| ECE 595 | Internet of Things: Hands on Applications | 3 |
| ECE 595 (1h) | Probability Theory and Stochastic Processes | 3 |
| ECE 595 (2h) | Optimal Estimation and Filtering | 3 |
Learning Outcomes
Students receiving master's degrees in computer engineering with a specialization in Applied Machine Learning and AI Systems Engineering will:
- Apply the mathematical foundations of machine learning systems and deep learning methods.
- Develop Applications of Machine learning and AI techniques, such as for processing digital images, videos, text, and audio signals.
- Develop AI and Machine Learning expertise through project-based learning.
