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Master of Science in Artificial Intelligence

College of Computing and Informatics
Computer Science
Study System
Thesis and Courses
Total Credit Hours
33 Cr.Hrs
2-4 Years
Fall & Spring
Sharjah Main Campus
Study Mode
Full Time and Part Time

Master of Science in Artificial Intelligence


The Master of Science in Artificial Intelligence Program (MSCAI) aims to offer the graduate student foundational and advanced knowledge in the topics related to Artificial Intelligence (AI).

The program is intended to accept incoming students from varied computing related undergraduate programs. Keeping in mind that incoming students may not have foundational knowledge, the program offers the needed background in the required remedial courses before embarking on advanced topics in the electives.

The program is offered in a one-track mode, which is thesis-based. The program requires completion of a substantial research component culminated with a formal thesis (equivalent to 9 credit hours of course work). The program has a compulsory component that ensures a common knowledge base for all students. The compulsory courses were selected to match the minimum compulsory knowledge that every student needs. Then an appropriate group of elective courses were selected to offer students variety of depth topics for specialization.

Program Objectives

Upon the successful completion of the program, student will be able to:
  1. Integrate scientific and technological principles underlying the Artificial Intelligence field.
  2. Apply the research process to critically evaluate Artificial Intelligence solutions to practical problems.
  3. Use professional tools and current techniques to specify, design, and implement Artificial Intelligence solutions.
  4. Communicate technical information, both orally and in writing proficiently.
  5. Review and critically evaluate recent developments and current research issues in the Artificial Intelligence field.
  6. Determine the legal and ethical principles that govern the Artificial Intelligence field and research work to make informed judgments.

Special Admission Requirements

The rules and regulations of the College of Graduate Studies are articulated in two publications: the Graduate Studies Bylaws and the Executive Regulations for the Master's Programs. Based on these rules and regulations, the Graduate Studies Committee in the Department of Computer Engineering grants regular enrollment for applicants to the MSCAI program who satisfy the following academic qualifications and criteria:

a.  The student must hold a bachelor's degree from a university recognized by the MOHE at the UAE with a minimum or equivalent CGPA of 3 on a 4-point scale. Students with a CGPA of 2.5 to 2.99 may be admitted conditionally provided that they register maximum (6) credit hours in the first semester of their study and obtain a "3" average. If he/she does not meet this requirement, the student is entitled to register only 3 credit hours in the second semester. The student will be expelled from the program, if he/she does not obtain "3" average at the end of the second semester.

b. The bachelor's degree must be in a major that enables the student to study the master's program.

c. A student may be admitted in a program other than his/her major upon the recommendation of the Departmental council, the College council, and the approval of the University's Graduate Studies Council. Additionally, the Department shall determine the prerequisite courses the student must take in a period not to exceed two semesters or a maximum of 24 credit hours. Such prerequisites must be taken before the master's courses. No prerequisite courses shall be counted in the calculation of the CGPA or the time limit set for graduation. For each 12 prerequisite credits taken by the student, a semester shall be added to the time limit set for completing the program

d.  Attendance in the bachelor's degree must not be less than 75% of the total hours required for graduation. The Council may admit students with degrees obtained by distance learning in special circumstances.

e.  Meeting the TOEFL condition:

1) Students in programs taught in English: The student must obtain 550 points on the TOEFL test or 6 on the IELTS.

f.   Native speakers of English shall be exempted from the TOEFL Test if the language of instruction in their undergraduate studies was English. Also, Students who graduated from academic institutions that use English as the primary medium of instruction, in bachelor studies, are exempted.The Departmental council may, with the approval of the University's Graduate Studies Council, stipulate additional conditions for admissions and re-admissions.

Program Structure & Requirements

Requirements Compulsory Elective Total
Courses Credit Hours Courses Credit Hours Courses Credit Hours
Courses 5 15 3 9 8 24
1 9 - - 1 9
Total Credit Hours 24 9 33

Study Plan
Study Plan: Course List
  1. Compulsory courses (15 credit hours)
  2. Elective Courses (9 credit hours)
  3. Thesis (9 credit hours)
Course Code Course Title اسم المساق
Credit Hours Pre-requisite
  Compulsory Courses ​ ​ ​
1501511 Advanced Programming البرمجة المتقدمة 3 Basic programming course
1501530 Advanced Artificial Intelligence الذكاء الاصطناعي المتقدم 3 Graduate standing
1501531 Machine Learning التعلم الآلي 3 (1440211 + 1501215) or equiv
1501566 Foundations of Data Science اساسيات علم البيانات 3 (1511263 or 1501567 or equiv.) And 1440281.
1501590 Research Methodology طرائق البحث 3 (1501215 or 1501501) and Graduate Standing
1501692 Thesis in AI أطروحة الذكاء الاصطناعي 9 Completion of 12 credit hours
  Elective Courses ​ ​ ​
1501533 Evolutionary Computing الحوسبة التطورية 3 1501530
1501535 Computer Vision & Image Processing الرؤية الحاسوبية ومعالجة الصور 3 1501511
1501565 Data Mining التنقيب عن البيانات 3  (1501263 or 1501567 or equiv.)
1501572 Computational Geometry الهندسة المحوسبة 3 1501372/1501511
1501630 Natural Language Processing معالجة اللغات الطبيعية 3 1501531
1501631 Directed Studies الدراسات الموجهة 3 Graduate standing
1501635 Computational Robotics الروبوتات المحوسبة 3 1501372/1501511
1501636 Applications of Deep Learning Networks تطبيقات شبكات التعلم العميق 3 1501531
1501638 Topics in Machine Learning موضوعات في تعلم الآلة 3 1501531
1501639 Topics in AI موضوعات في الذكاء الاصطناعي 3 1501530
1501641 Applied Human Computer Interaction تفاعل الإنسان التطبيقي مع الحاسوب 3 Graduate standing
1501668 Big data & Data Analytics البيانات الضخمة وتحليلات البيانات 3 1501263 or 1501567 or equiv.

Study Plan

​First Year
Fall Semester ​ ​   Spring Semester ​ ​ ​  
Crs. Code Course Title Type Hrs Crs. Code Course Title Type Hrs
1501511 Advanced Prog. C 3 1501531 Machine Learning C 3
1501530 Advanced AI C 3 1501590 Research Methodology C 3
1501566 Found. of Data Science C 3   Elective Course E 3
Total 9 Total 9
Second Year ​ ​ ​ ​ ​ ​ ​ ​
Fall Semester ​ ​   Spring Semester ​ ​ ​  
Crs. Code Course Title Type Hrs Crs. Code Course Title Type Hrs
  Elective Course E 3 1501692 Thesis in AI C 6
  Elective Course E 3        
1501692 Thesis in AI C 3        
9 Total 6

Study Plan: Course Distribution

Course Description
1501511 Advanced Programming 3
Prerequisite: Basic programming course

This course aims to familiarize students with advanced methods in programming using Python, including object-oriented programming, parallel programming, data structures, algorithms, and applications of Python programming to AI and Data Science. The course covers the following major modules: (I) Object-Oriented Programming, (II) Data Structures and Algorithms, (III) Parallel Programming, (IV) Applications and Computing Tools: Features, Libraries, agents and controllers, and Regular Expressions applied to NLP tasks. ​ ​
1501530 Advanced Artificial Intelligence 3
Prerequisite: Graduate standing

This course covers fundamental and advanced concepts and techniques in the field of artificial intelligence. The main topic list includes intelligent agents, uninformed and informed search, adversarial search, constraint satisfaction problem, and uncertain knowledge and reasoning, which covers Bayesian Networks, and Decision Networks. In addition, advanced topics will include machine learning, reinforcement learning, natural language processing (or vision/robotics), and deep learning. ​ ​
1501531 Machine Learning 3
Prerequisite: (1440211-Linear Algebra + 1501215-Data Structures) or equivalent

This course provides a broad introduction to machine learning. Main topics include Regression, classification, and clustering. Detailed subjects are simple and multiple, Ridge, kernel feature, feature selection & Lasso; Linear classifiers & logistic regression; decision trees and ensemble learning, support vector machines, and artificial neural networks. Besides, best practices in machine learning such as overfitting/regularization and bias/variance theory shall be covered. Students will learn how to identify and implement appropriate machine learning algorithms for a variety of problems. ​ ​
1501533 Evolutionary Computing 3
Prerequisite: 1501530 Advanced Artificial Intelligence

This course introduces the main concepts, techniques and applications in the field of evolutionary computing. Topics covered include, components of Evolutionary Algorithms, Genetic Algorithms, Evolution Strategies, Genetic Programming and Learning Classifier Systems, constraint handling, multi-objective cases, Nonstationary and Noisy Function Optimisation, Coevolutionary Systems, Interactive Evolutionary Algorithms, Theory of evolutionary computing, Hybridisation with Other Techniques: Memetic Algorithms, and Ant colony optimization. ​ ​
1501535 Computer Vision & Image Processing 3
Prerequisite: 1501511 Advanced Programming

Introduction to the basic and advanced concepts and techniques in computer vision and image processing. After completing this course, the students will be able to apply a variety of computer techniques for the design of efficient algorithms for real-world applications, such as optical character recognition, face detection and recognition, motion estimation, human tracking, and gesture recognition. The topics covered include image filters, edge detection, feature extraction, object detection, object recognition, tracking and motion analysis, gesture recognition, image formation and camera models, and stereo vision. The course will cover the deep learning concepts with introduction to various architectures and their applications. ​ ​
1501565 Data Mining 3
Prerequisite: Introduction to Database Management Systems (1501263), or 1501567 or equivalent.

Data mining has become one of the most interesting and rapidly growing fields. Data mining techniques are used to uncover hidden information, such as patterns, in databases and perform predictions. The data to be mined may be complex data including multimedia, spatial, and temporal. Topic include data processing, association rules, clustering, and classification. This course is designed to provide graduate students with a solid understanding of data mining concepts and tools. ​ ​
1501566 Foundations of Data Science 3
Prerequisite: Introduction to Database Management Systems (1501263) or 1501567 or equivalent, and Introduction to Probability and Statistics (1440281)

Data science is an interdisciplinary field that provides tools to extract insights from data in various forms, either structured or unstructured. Data science course provides the theories, strategies, and tools to understand and apply the following topics: data preparation, data cleaning & integration, data analysis, classification, clustering, text analysis, and visualization. ​ ​
1501572 Computational Geometry 3
Prerequisite: 1501371: Algorithm Analysis & Design or 1501511 Advanced Programming

The preliminary topic list includes: finding the convex hulls, art gallery problems, computing Voronoi diagrams, line segment intersection, linear programming, point location, randomized algorithms, and computing a delauney triangulation. In addition, we will learn about the following data structures: k-d trees, range trees, interval trees, segment trees, and quadtrees. ​ ​
1501590 Research Methodology 3
Prerequisite: 1501215-Data Structures or equivalent, and Graduate Standing

This course introduces graduate students to the practice of research. The course preliminary introduces students to concepts of research methods in data science, data resources, data collection, and literature review. The course ensure that students learn how to select a research topic, devise research questions, and plan the research. Additionally, the students will gain practical knowledge on technical writing, such as writing a thesis proposal, a survey paper, and technical review of research papers. Students will also gain the skills, and practice, of technical presentations of scientific research papers and proposal. Lab sessions will be conducted to train students on a Latex editor for writing technical reports. ​ ​
1501630 Natural Language Processing 3
Prerequisite: 1501530 Advanced AI

This course provides a broad coverage of the field of Natural Language Processing (NLP) throughout the study of the models, methods, and algorithms of NLP for common NLP problems. Topics include Regular Expressions, N-gram Language Models, Naive Bayes, Sentiment Classification; Lexicon for Sentiments, Vector Semantics and Embeddings, Neural Networks and NL Models, Part-of-Speech Tagging; Sequence Processing, Encoder-Decoder Models, Constituency Grammers and Parsing, Part of Speech Tagging, Machine Translation, Question Answering, Sentiment Analysis, and Text Summarization. ​ ​
1501631 Directed Studies 3
Prerequisite: Graduate Standing

This course helps the student in exploring specific areas of interest or enables him/her to develop in-depth research in a field of interest. The topic should be related to the area of interest in which the student is planning to prepare his/her thesis. The course intends to complete the knowledge of the student while allowing him/her to develop his/her critical thinking and analysis. The registration in this course and its topic should be approved in advance by the student's potential thesis supervisor and the program coordinator. ​ ​
1501635 Computational Robotics 3
Prerequisite: 1501371: Algorithm Analysis & Design or 1501511: Advanced Programming

The course provides an overall coverage of computational and algorithmic aspects of robotics with an emphasis on the motion planning problem. The preliminary topics include position & orientation in 2D and 3D, time and motion, robot forward kinematics, inverse kinematics, mobile robot vehicles, reactive navigation, map-based navigation, recent navigation algorithms, localization, localization: EKF and Monte-Carlo, probabilistic roadmaps and motion planning; pursuit-evasion algorithm, and Recent Advancements in the Field. ​ ​
1501636 Applications of Deep Learning Networks 3
Prerequisite: 1501531 Machine Learning

This course provides a coverage of several application areas in AI that use Deep Learning (DL) networks. Topics include Python preliminaries, Keras and TensorFlow, handling big data, Regularization and Dropout,  Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), Generative Adversarial Networks (GANs), Transfer learning, Reinforcement Learning, Applications in computer vision, applications in NLP, and evolving other deep Neural Networks such as Transformers. ​ ​
1501638 Topics in Machine Learning 3
Prerequisite: 1501531 Machine Learning

This advanced graduate course explores in depth several important topics in machine learning. The contents will vary depending on the topic. ​ ​
1501638 Topics in Artificial Intelligence 3
Prerequisite: 1501530 Advanced Artificial Intelligence

This advanced graduate course explores in depth several important topics in artificial intelligence. The contents will vary depending on the topic. ​ ​
1501641 Applied Human Computer Interaction 3
Prerequisite: Graduate standing

This course is aimed to introduce students to fundamentals of HCI together with its application in novel UI design and development using state-of-the-art interaction mechanisms. The course covers the concepts, methods, and techniques in planning, designing, prototyping, and evaluating user interfaces for interactive systems. Topics include design principles, usability principles and engineering, solving user-centered problems, device interaction, and graphical user interface design (2D and 3D interfaces). For the application, the course introduces development concepts for controller-based UI design for desktop, mobile, and virtual reality. In addition, the course also introduces the design and development of controller-free natural user interface design and development for aforementioned systems. ​ ​
1501668 Big data & Data Analytics 3
Prerequisite: : Introduction to Database Management Systems (1501263) or 1501567 or equivalent

Big data is becoming one of the most important technology that enables organizations to store, manage, and manipulate vast amounts of data efficiently to gain business insights. Big data course provides the fundamentals, technologies, and tools to understand and apply the following the Big Data analytics. Topics cstuovered are: Big Data types, technologies, analytical tools, numerical, textual, image and stream analysis, and applications of spatial data and remote sensing. ​ ​
1501692 Thesis in Artificial Intelligence 9
Prerequisite: : Completion of 12 credit hours

A comprehensive research project carried out individually under the supervision of one or more faculty members. The work involves original research leading to the solution of a research problem, and should be publishable in the form of a research paper. The thesis has to be defended in front of an examination committee to achieve the pass grade. ​ ​

Special Admission Requirements
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