Master of Science in Machine Learning
The Master of Science in Machine Learning (MSMLR), offered by the Department of Computer Science and Engineering at AUS, will prepare students for advanced careers and/or doctoral studies in disciplines related to machine learning (ML).
Machine learning is an essential component of artificial intelligence (AI) and one of the most important emerging fields. It is used in countless and diverse areas including autonomous vehicles, smart drones, satellites, avionics, medical diagnosis, drug design, security, mobile phone applications and home appliances. ML also plays a key role in improving services such as decision-making, flight booking, online shopping, web search, social networks, health care, market analysis and much more. It is the technology that paved the way for groundbreaking systems like ChatGPT and other similar innovations.
AI and ML are critical tools for achieving the UAE government’s vision of a knowledge-based economy that focuses on innovation and advanced research to fulfill the needs of the nation, its people and the wider international community. The MSMLR program will help disseminate innovative knowledge, concepts and ideas to the local and international community. Through this program, students will develop the necessary research and leadership skills, with a strong emphasis on scholarship, teamwork and professional ethics in a global and societal context.
Based on high standards similar to those followed in the United States, the MSMLR curriculum provides courses in advanced machine learning, big data and analytics, generative deep learning, pattern classification, data and text mining, hardware architectures for ML, cognitive robotics, mobile ML, computer vision, natural language processing, ML for software engineering and biomedical imaging and informatics.
Find our program brochure here
Our master of science programs offer you the flexibility to choose one of two paths:
- Course-Only Option: Complete 10 courses
- Thesis Option: Complete 7 courses and a thesis
MISSION STATEMENT
The mission of the Master of Science in Machine Learning (MSMLR) program at AUS is to prepare professionals for advanced careers and/or doctoral studies related to machine learning. The program strives to create a stimulating academic environment that promotes excellence in teaching and research to assist the students in becoming competent, innovative and responsible professionals with effective communication skills.
PROGRAM EDUCATIONAL OBJECTIVES
The objectives of the Masters of Science in Machine Learning program are to produce graduates who will:
- be successful professionals in a specialized area of machine learning
- maintain a desire for research, innovation and lifelong learning
- uphold the responsibilities of the engineering profession
STUDENT OUTCOMES
Upon graduation, an AUS MSMLR graduate should be able to:
- perform research emphasizing creativity, independent learning and scientific methods in the field of machine learning
- apply advanced mathematics, computer science knowledge and software tools in identifying, formulating and solving real-world problems
- demonstrate an in-depth understanding of modern machine learning approaches, algorithms and tools
- select and use techniques, skills and modern tools necessary for research or professional practice
- communicate effectively through technical presentations and reports
- recognize the need for, and engage in, lifelong learning in professional areas
- attend to professional and ethical responsibilities within global and societal contexts
If our Master of Science in Machine Learning is your choice, you can apply here now.
Degree Plan
What are our admission and program requirements? What courses are taught in our program? Do students need to complete a master's thesis? The links and information below provide answers to these and other questions that you may have.
ADMISSION REQUIREMENTS
AUS strives to attract students with excellent qualifications in order to maintain the world-class quality of its programs. In addition to meeting the university's general graduate admission requirements, an applicant to the MSMLR program must hold a Bachelor of Science in Computer Science or Computer Engineering from an independently accredited university recognized by the UAE Ministry of Education's Higher Education Affairs Division and by AUS (equivalent to QFEmirates Level 7). Students with a bachelor’s degree in engineering fields or a quantitative science field that is closely related to computer science, and who possess a strong programming competency, may be considered on a case-by-case basis.
For more information, contact msml@aus.edu.
REQUIREMENTS AND COURSE DESCRIPTIONS
Students pursuing the MSMLR degree must successfully complete a minimum of 30 credit hours with a minimum cumulative grade point average of 3.00 out of 4.00. Students must complete the degree requirements within five years from the time of initial enrollment in the program.
More specifically, students in the MSMLR program must successfully complete the following requirements, which are aligned with internationally recognized graduates programs in machine learning, especially those in the United States:
Required Courses (six credit hours)
Students must successfully complete six credit hours of required program core courses:
- MLR 555 Advanced Artificial Intelligence
- MLR 570 Advanced Machine Learning
A zero-credit hour seminar
Nine credit hours in Master’s Thesis
Core Electives (minimum of nine credit hours)
Students must successfully complete at least three courses from the following list:
- MLR 503 Data Mining and Knowledge Discovery
- MLR 506 Hardware Architectures for Machine Learning
- MLR 508 Cognitive Robotics
- MLR 510 Generative Deep Learning
- MLR 511 Mobile Application Development with Machine Learning
- MLR 512 Advanced Computer Vision
- MLR 513 Advanced Natural Language Processing
Breadth Electives (minimum of six credit hours)
Students must successfully complete at least two courses from the following list:
- COE 505 Cloud Computing Infrastructure
- COE 555 Cyber Security
- COE 594-09 Modeling and Testing in Software Engineering
- COE 636 Advanced Multicore and GPU Computing
- BME 543 Biomedical Imaging Technologies
- BME 581 Biomedical Informatics
