Master of Science in Artificial Intelligence program offered by Ajman University is the first of its kind in the country. We will be one of the leading Universities in the region to offer Master in Artificial Intelligence. Artificial Intelligence (AI) is the newest trend in computer science nowadays. Although (AI) is not new as science, it is recently attracting the attention of business leaders, industry, military, and governments from all over the world. The component of mimicking the human ability that AI has, such as; inference, deduction, knowledge aggregation, learning, recognition and even expressing emotions make AI based technology of great interest. It is extremely important nowadays for our community and organizations to be at the leading edge of technologies such as AI. This will empower all organizations of the nation, and make them at the top niche of advancement and development.
The mission of the Master of Science in Artificial Intelligence (MSAI) is to prepare graduates with well-rounded education in the different areas of Artificial intelligence (AI). The graduates will be able to develop AI technologies and fulfill the local and regional market’s needs. The program will motivate scientific research in this field in collaboration with internal and external entities.
The Education Objectives of the MSAI program are to graduate students who will be able to:
Students who have completed undergraduate education in an English-medium institution might be allowed admission into a graduate program without demonstrating TOEFL score of 550 (or equivalent). This exemption can be applicable only to those students who undertook all their schooling (K-12) plus a Bachelor’s degree in English in a reference English speaking country (e.g. UK, USA, Australia, New Zealand);
Applicants may be conditionally admitted to the MSAI program if they have a recognized Bachelor’s degree and an EmSAT score of 1250 or its equivalent on another standardized test approved by the CAA, such as TOEFL score of 530 (197 CBT, 71 iBT), or 5.5 IELTS academic. Such a student must meet the following requirements during the period of conditional admission or be subject to dismissal:
Meeting the above conditions does not guarantee automatic admission into the program. Authority for admitting an applicant for the MSAI program is vested in the Dean of College of Engineering and Information Technology and the Dean of the Graduate Studies and Research. By written communication, both Deans will transmit the decision to the applicant, and the registrar.
The Program Coordinator recommends transfer of credits to the Dean who will forward it to the Registrar who makes appropriate changes to the student transcript. A student enrolled at Ajman University who wishes to take courses at another institution with the intention of transferring them to AU must have the prior written permission of the Program Coordinator and the Dean before registering for such courses. Details about the required documents for admission are available in Graduate Student Catalog.
The completion requirements of the Master’s degree in Artificial Intelligence shall be:
On successful completion of this program the graduate will be able to:
PLO#1: Demonstrate an in depth understanding of the theories and techniques of AI.
PLO#2: Review and contrast new research findings and developments in the AI field.
PLO#3: Integrate diverse AI technologies to formulate an innovative solutions to a complex problems.
PLO#4: Analyze and evaluate critically AI-based solutions to a highly complex problems.
PLO#5: Manage and take responsibility for conducting AI-based research/project development.
PLO#6: Function independently and collectively as a member of a team and assume leadership roles.
PLO#7: Resolve highly complex ethical and societal issues arising from implementing AI-based solutions.
Artificial Intelligence – MAI602
The aim of this course is to provide graduate students with in-depth knowledge of AI principles, algorithms and techniques. Topics covered include Knowledge Representation schemes and Automated Reasoning, uncertain knowledge and probabilistic reasoning, search strategies, intelligent agents, machine learning, planning, and ethical and societal issues relating to artificial intelligence. Students also work on a course project individually or in pairs.
Biomedical informatics – MAI611
This course presents the unique characteristics of biomedical data, clinical data and the representation methods of life science; data, information, and knowledge. All utilized to improve human health and provide wider understanding of biomedical data related to health care. It covers basic concepts and provide solid background for research related to the exploration of computational and analytical aspects of biomedical information systems. It encompasses topics such as information sharing and management, clinical informatics and decision-making, clinical information retrieval, imaging informatics, patient monitoring systems, cognitive science/natural language processing in bioinformatics, and translational applications of informatics. There will be hands-on assignments and term paper to familiarize students with those concepts.
Business Intelligence – MAI610
Business intelligence (BI) is a broad term that includes set of methodologies, processes, and technologies that transform raw data into meaning information and knowledge that can be used by decision makers. This course aims to provide students with broad and in-depth knowledge of Business Intelligence (BI). Topics covered include, basic concepts of BI, Data warehousing concepts and requirements, business performance management, descriptive analytics, predictive analytics, prescriptive analytics, Decision Support Systems, Knowledge Management, Big Data/data mining and text mining, and data visualization. Students participate in the development of group project that embodies the solution of business problem using BI techniques.
Computer Vision and Pattern Recognition – MAI609
This course provides a broad coverage of the fundamental techniques of image processing and computer vision. Course topics include: essential mathematical background, image geometry, image processing operations and filters, edge detection, segmentation, image features extraction, object recognition techniques, and synthesis and analysis. Students also work on group projects (2 to 3 students) to create an object recognition software component or system.
Data Mining – MAI601
Data mining is the process of discovering patterns and knowledge from huge amount of dataset. This course aims to equip students with the necessary skills and knowledge that allow them to develop models using data mining techniques that include association, clustering, outlier, web mining, text mining, and pattern mining approaches. Students will also learn to collate, filter, clean, transform, and sort data using established contemporary tools. Validation and performance assessment is applied to compare test data with training data and assess accuracy of processes and models.
Evolutionary Computations – MAI607
This course will cover topics in evolutionary computations and their application to solve optimization problems. Course topics include: basics of genetic algorithms; constrained optimization; Multimodal Optimization; Multiobjective Optimization; Swarm Intelligence; Genetic Programming; and Combinatorial Optimization. Students also are required to review research on evolutionary computation and work in group projects (2 to 3 students) to solve a problem using evolutionary-based algorithms.
Human Computer Interactions – MAI608
This course provides students with the necessary knowledge and skills needed to design and implement interactive computer systems using the latest human computer interaction (HCI) principles and theories. Topics covered by this course include, general overview of HCI, universal usability, guidelines, principles, and theories of HCI, managing design processes, evaluating design, Interaction styles, devices, communication and collaboration, critical design issues, search and visualization.
Machine Learning – MAI603
This course aims to provide students with an in-depth introduction to the main areas of machine learning. Topics covered include, supervised and unsupervised learning models and algorithms in classification, regression, and clustering, reinforcement learning algorithms and models, genetic algorithms in machine learning, and model selection and evaluation. Students also work in group projects (2 to 3 students) that embodies the solution to a machine learning problem.
Master Project – MAI698
Student is required to plan, design, build, and test a high functionality project in coordination with a project supervisor. The student should use the experience and knowledge gained from preceding courses taken earlier to improvise and build an AI based application that has great potential of being transformed into a commercial asset. Research component is highly recommended in the master project especially if it constituted a heuristic or technological addition. The project requires a written proposal, a proposal presentation, and a final presentation.
Master Thesis – MAI699
Students are required to investigate some contemporary AI related topic, prepare a high quality proposal, and write a high quality manuscript with content and organization that meet international standards. Students should follow the rules and regulations set by the MSAI council for that purpose. All work should be done in coordination with a thesis supervisor. The thesis must constitute original contributions, in the form of theories or heuristics, to the fields of AI and its applications. There will be an oral presentation for the submitted proposal and oral defense for the thesis. Manuscript of the thesis will be archived and copyrighted.
Natural Language Processing with Deep Learning – MAI612
Fundamental concepts of Natural Language Processing (NLP) and deep learning methods are introduced. Word level syntactic and semantic processing from linguistic and algorithmic perspectives are discussed. Statistical acquisition and modern quantitative techniques are presented. Deep learning neural networks paradigms are introduced. Deep Learning techniques in NLP utilizing different learning methods and architectures such as Multi-layer perceptron, convolutional, encoder-decoder, Greedy-Wiser DBN will be used in solving underlying problems. Comparisons with classical methods (HMM/statistical) are presented. Corpuses preparation is presented. A research or application oriented project in text and/or speech recognition will be conducted.
Robotics – MAI605
Methods of analysis for operations of robotics are presented. The manipulators dynamics and kinematics including trajectory planning along with motion control, vision, and sensing are covered. Programming to control robots using hardware interfaces (microcontrollers) for motion and motion planning along with task assembling. Optimum trajectory and optimum grippers are presented. Uncertainty and stability issues in grasping and planning. Applications of robots in several areas of real life. Hybrid AI and robotics techniques. Lab work will provide hands on experience.
Special Topics in Artificial Intelligence – MAI613
The course should demonstrate an in depth understanding of the theories of AI techniques and applications in a relevant field. Students also need to be able assess and critique some research findings in that AI field. They will integrate AI technologies to improvise innovative solutions for some complex problem. During the course, they must function independently and collectively and take full responsibility to complete a research/project development. They should resolve complex ethical issues when implementing AI- based solutions.