Master of Science in Machine Learning

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

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.

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