Master of Science [M.Sc] (Computer Science) From Khalifa University
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Master of Science [M.Sc] (Computer Science)

Khalifa University

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Program Overview

  • The Master of Science in Computer Science degree is awarded for successfully completing the requirements of a program of study, which includes taught courses as well as thesis.
     

  • The thesis is an independent investigation of specialized areas within the general field of computer science and associated disciplines.
     

  • This program gives candidates the opportunity to deepen their knowledge in the broad field of computer science and contribute to the process of discovery and knowledge creation through the conduct of original research.
     

  • Students for this degree are taught and supervised by experienced faculty and are expected to demonstrate initiative in their approach and innovation in their work. 

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  Location

Abu DhabiUnited Arab Emirates

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  Course Duration

24 Months

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  Tuition Fee

AED 89,181

 Score

IELTS: 6.5 GRE: 300 TOEFL: 91

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A student graduating with the MSc in Computer Science will be able to:
 

  • Identify, formulate, and solve advanced computer and information systems problems through the application of modern tools as well as techniques and advanced knowledge of mathematics and science.

  • Acquire knowledge of contemporary issues in the field of computer science.

  • Design and conduct experiments, as well as analyze and interpret data and make decisions.

  • Conduct research and document and defend the research results.

  • Function on teams and communicate effectively.

  • Conduct themselves in a professional and ethical manner.
     

    The MSc in Computer Science (CS) program consists of a minimum of 36 credit hours. The required program credits are distributed as follows: 12 credits of Program Core courses, 12 credits of Program Elective courses, and 12 credits of CS Master’s Thesis work. 
     

    A student may organize the selection of the elective courses and the master’s thesis topic to follow a concentration either in “Artificial Intelligence” or “Space Systems and Technology” within the broad field of computer science. In such cases, the optional concentration will be noted on the student’s transcript and diploma (degree certificate). The table below presents a summary of the MSc in CS degree program structure and requirements.  All the MSc in CS program courses (with the exception of the Research Seminar, the Master’s Thesis, and the lab courses in the Space Systems and Technology Concentration) have a credit rating of three credits each.
     

    Summary of MSc in Computer Science Degree Program Structure and Requirements
     

    Category

    Credits Required

    Program Core

    12

    Program Electives

    12

    Computer Science Master’s Thesis

    12

    Total

    36


    Students seeking the degree of MSc in CS must successfully complete a minimum of 36 credited hours as specified in the categories detailed in this section, with a minimum Cumulative Grade Point Average (CGPA) of 3.0.
     

    The MSc in CS degree program core requires a minimum of 12 credits, consisting of 3 credits of mathematics, 9 credits of computer science core courses, and the research seminar course which has zero credit rating.  The courses for each one of the core categories are specified below.

     

  • Mathematics Core Courses (3 credits)

  • Students must select at least one course from the list below:

  • ENGR 602 Engineering Numerical Methods

  • ENGR 605 Systems Optimization

  • Computer Science Core Courses (3 credits x 3 = 9 credits)

  • Students must select at least three courses from the list below:

  • COSC 602 Software Engineering

  • COSC 604 Artificial Intelligence

  • COSC 607 Algorithm Design Techniques

  • COSC 608 Distributed Systems and Cloud Computing

  • CSEC 632 Advanced Operating Systems

  • Note: if a student takes more than 3 courses, the additional one(s) will be counted as equivalent to taking 3-credit program elective course(s).

  • ENGR 695 Seminar in Research Methods (0 credits)


    Program Electives
     

  • Students must complete a minimum of 12 credits of electives.

  • All the elective courses must be the ones offered by the Department of Electrical Engineering and Computer Science (i.e., those with COSC 6XX, ECCE 6XX, or CSEC 6XX course codes) among which at least two must be from the list below.

  • (Note however, as an exception, based on the student’s research requirements, the student may take at most two M.Sc.-level courses from outside of the Department of Electrical Engineering and Computer Science with the permission and justification of his/her thesis advisor.)

  • COSC 603 Multi-agent Systems

  • COSC 605 Strategic Requirements Engineering

  • COSC 606 Machine Learning

  • COSC 620 Algorithms in Bioinformatics

  • COSC 621 Data Science

  • COSC/ECCE 631 Blockchain Fundamentals and Applications

  • COSC/ECCE 635 Deep Learning System Design

  • COSC/ECCE 636 Human Computer Interaction

  • COSC/ECCE 637 Parallel Programming

  • COSC/CSEC 638 Artificial Intelligence Techniques for Cyber-Security

  • COSC 694 Selected Topics in Computer Science
     

    Master’s Thesis
     

    A student must complete a master’s thesis that involves creative, research-oriented work within the broad field of computer science, under the direct supervision of at least one full-time faculty advisor.  The research findings must be documented in a formal thesis and defended successfully in a viva voce examination. 
     

    Concentrations
     

    A student may opt to have one of the optional concentrations within the MSc in CS program.  To do so, the student must complete a minimum of 9 credits from the elective courses specified for the particular concentration and conduct research for her/his thesis within the domain of that concentration.  The following concentrations are currently available under the MSc in CS program:

     

  • Artificial Intelligence

  • Space Systems and Technology

  • Artificial Intelligence Concentration

    The requirements for a student who would like to pursue the Artificial Intelligence Concentration are as follows:

  • The student must take at least 9 credits from the courses below.

    • COSC 603 Multi-agent Systems

    • COSC 606 Machine Learning

    • COSC 621 Data Science

    • COSC/ECCE 635 Deep Learning System Design

    • COSEC/CSEC 638 Artificial Intelligence Techniques for Cyber-Security

  • The student’s thesis must be in the general domain of Artificial Intelligence.

  • Space Systems and Technology Concentration

    The requirements for a student who would like to pursue the Space Systems and Technology Concentration are as follows.

  • The student must take all 9 credits of the following 1 course (3 credits) and 3 labs (3 x 2 credits = 6 credits).

    • SSCC 601 Spacecraft Systems and Design (3 cr.)

    • SSCC 602 Spacecraft Systems Lab – 1 (2 cr.)

    • SSCC 603 Spacecraft Systems Lab – 2 (2 cr.)

    • SSCC 604 Spacecraft Systems Lab – 3 (2 cr.)

  • The student must take one of the other MSc in CS program elective courses.

  • The student’s thesis must be in the general domain of Space Systems and Technology.
     

    COURSE DESCRIPTIONS
     

    COSC 602 Software Engineering (3-0-3)
     

    Prerequisite: Undergraduate course in Software Engineering (or equivalent).

    This course is an advanced course on software engineering, which deals with the advanced topics in quality requirements for mission-critical systems, large-scale software architecture, and data mining of software engineering repositories and artifacts. Topics include mission critical non-functional requirements safety, security, privacy, and trust; large-scale software architecture patterns and re-structuring; data mining error logs, and other selected topics.

     

    COSC 603 Multi-Agent Systems (3-0-3)
     

    Prerequisite: Undergraduate course in artificial intelligence (or equivalent).

    This course is an advanced course on multi-agent systems, which deals with the analysis and design of distributed entities that interact with each other in both cooperative and non-cooperative domains. Topics include: cooperative and non-cooperative game theory, social choice, mechanism design, auctions, repeated games, distributed optimization, multi-agent learning and teaching, and other selected topics.

     

    COSC 604 Artificial Intelligence (3-0-3)
     

    Prerequisite: Undergraduate course in artificial intelligence.

    This course is a graduate-level introduction to the field of artificial intelligence (AI). It aims to give students a solid understanding of the main abstractions and reasoning techniques used in AI. Topics include: representation and inference in first-order logic; modern deterministic and decision-theoretic planning techniques; Bayesian network inference and (Deep) Reinforcement Learning.

     

    COSC 605 Strategic Requirements Engineering (3-0-3)
     

    Prerequisite: Undergraduate course in software engineering (or equivalent).

    This is an interdisciplinary graduate-level course on requirements engineering and the application of requirements engineering principles and techniques to the development of complex socio-technological systems. The course puts particular emphasis on the integration of economic, strategic, social, and technological requirements, and the analysis of their impact on the future evolution of the system.

     

    COSC 606 Machine Learning (3-0-3)
     

    Prerequisite: Undergraduate course in machine learning (or equivalent).

    This course will cover graduate-level materials on machine learning in both theory and practice by building upon the undergraduate-level course on “Introduction to Machine Learning”. The topics include statistical learning theory, ensemble learning, probabilistic learning, dimension reduction, recommender systems, advanced clustering, semi-supervised learning, transfer learning, etc.

     

    COSC 607 Algorithm Design Techniques (3-0-3)
     

    Prerequisite: Undergraduate course in design and analysis of algorithms (or equivalent).

    Algorithms constitute the core of Computer Science and algorithm design is crucial for the performance of real-world software systems. This is an advanced algorithms course, focusing on techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics include average case analysis of search trees and hashing; amortized analysis; competitive analysis; parallel algorithms; approximation algorithms for hard optimization problems, algorithms for problems arising in computational geometry and number theoretic algorithms.

     

    COSC 608 Distributed Systems and Cloud Computing (3-0-3)
     

    Prerequisite: Undergraduate courses in computer networks and parallel and distributed computing (or equivalent).

    This course teaches in-demand technologies for distributed and parallel computation as well as storing and processing large amounts of data using cloud computing technologies. While underlying network and architecture issues are discussed to the extent that enables a basic understanding, particular focus is on the data science aspects of Cloud computing and cloud applications complementary to other Computer Science courses related to the realm of Data Science and Artificial Intelligence. It introduces general concepts and deploys the state-of-the-art systems from public cloud systems, but also instructs how to use locally available clouds.

    COSC 620 Algorithms in Bioinformatics / Cross-Listed with BMED 634 (3-0-3)
     

    Prerequisite: Undergraduate course in bioinformatics and genomic data science (or equivalent).

    This course focusses on algorithms to explore the many types of data produced in the life sciences, while combining theory and practice. The course teaches the students how to deal with DNA and protein sequence data algorithmically. We will develop software to find disease causing mutations in cancer etc., to understand what genes do and to elucidate human ancestry. Towards those goals, we deal with functional gene annotation, biological databases, comparative genomics, phylogenetics, forensics and structural bioinformatics.

     

    COSC 621 Data Science (3-0-3)
     

    Prerequisite: Undergraduate course in data analytics (or equivalent).

    This graduate-level course on data science builds upon the undergraduate course on “Data Analytics”. It covers the topics of big data methods, decision theory, data streams and online learning, time-series forecasting, and data science in different domains like string/sequence, text, image/video, and graph/network.

     

    COSC 631 Blockchain Fundamentals and Applications / Cross-Listed with ECCE 631 (3-0-3)
     

    Prerequisite: Undergraduate knowledge of Computer Networks or Communications Networks (or equivalent).

    Introduction to cryptocurrencies, wallets, and Blockchain; Blockchain key features, benefits, and popular use cases; Blockchain fundamentals, protocols, algorithms, and underlying infrastructure Building Ethereum and Hyberledger blockchains; Decentralized applications (DApps); Smart contracts; Trusted Oracles; Decentralized storage; Designing and architecting blockchain-enabled systems and solutions for applications in IoT, AI, Supply Chain Management and Logistics, Healthcare, Smart Grids, 5G networks, Telecommunication, etc. Cost and Security Analysis; Limitations and open research challenges in Blockchain.

     

    COSC 632 Advanced Operating Systems / Cross-Listed with ECCE 632 (3-0-3)
     

    Prerequisite: Undergraduate course in operating systems.

    The course presents the main concepts of advanced operating systems (parallel processing systems, distributed systems, real time systems, network operating systems, and open source operating systems), including the hardware and software features that support these systems.

     

    COSC 635 Deep Learning Systems Design / Cross-Listed with ECCE 635 (3-0-3)
     

    Prerequisite: Undergraduate knowledge of artificial intelligence (or equivalent).

    High level introduction to deep learning concepts and essential contexts, deep learning computational framework, system implementation practicalities, machine learning workflow, practical classification problems for different data modalities, state of the art deep learning models.

     

    COSC 636 Human Computer Interaction / Cross-Listed with ECCE 636 (3-0-3)
     

    Prerequisite: Undergraduate knowledge of software engineering.

    This course covers the principles of human-computer interaction, the design and evaluation of user interfaces. Topics include an overview of users’ needs and how cognitive aspects affect the design of user interfaces; the principles and guidelines for designing usable user interfaces, with emphasis on the different and novel interactions and trends in HCI; the interaction evaluation methodologies and techniques that can be used to measure the usability of software. Other topics may include World Wide Web design principles and tools, crowdsening/sourcing, speech and natural language interfaces, and virtual reality interfaces.

     

    COSC 637 Parallel Programming / Cross-Listed with ECCE 637 (3-0-3)
     

    Prerequisite: Undergraduate knowledge of programming in C, C++, Java or similar, data structures and algorithms, and basic computer architecture.
     

    This course is a hands-on introduction to parallel computing for MSc students with emphasis on the most common and accessible parallel architecture, namely, the Graphics Processing Unit (GPU). The course will introduce students to modern GPU architectures and the fundamental concepts of parallel computing, including data parallelism, scalable execution, memory and data locality, multithreading, and synchronization. The course will also cover some of the most common parallel patterns such as convolution, prefix sum, graph search, and sparse matrix multiplications, along with their GPU implementations. The case study of deep convolutional neural networks will be covered in detail. NVIDIA’s CUDA programming environment will be used throughout the course for homework assignments and the course project.

     

    COSC 638 Artificial Intelligence Techniques for Cyber Security / Cross-Listed with CSEC 638 (3-0-3)
     

    Prerequisite: Undergraduate course in artificial intelligence.

    This course provides student with a basic understanding of cybersecurity techniques incorporating Artificial Intelligence (AI) and Machine Learning (ML) technologies. Also, it outlines security and privacy issues of those systems.

     

    COSC 694 Selected Topics in Computer Science (3-0-3)
     

    Prerequisite: Will be specified according to the particular topics offered under this course number.

    This course covers selected contemporary topics in Computer Science. The topics will vary from semester to semester depending on faculty availability and student interests. Proposed course descriptions are considered by the Department of Electrical and Computer Science on an ad hoc basis and the course will be offered according to demand. The proposed course content will need to be approved by the Graduate Studies Committee. The Course may be repeated once with change of contents to earn a maximum of 6 credit hours.

     

    COSC 699 Master’s Thesis (minimum 12 credit hours)
     

    Corequisite: ENGR 695 Seminar in Research Methods, approval of the Department Chair and the Associate Dean for Graduate Studies.

    In the Master’s Thesis, the student is required to independently conduct original research-oriented work related to important computer science problems under the direct supervision of a main advisor, who must be a full-time faculty in the Electrical Engineering and Computer Science Department, and at least one other full-time faculty who acts as co-advisor. The outcome of the research should demonstrate the synthesis of information into knowledge in a form that may be used by others and lead to publications in suitable reputable journals/conferences. The student’s research findings must be documented in a formal thesis and defended through a viva voce examination. The student must register for a minimum of 12 credit hours of Master’s Thesis.

  • Students must complete a Bachelor’s degree in a relevant discipline with a minimum Cumulative Grade Point Average (CGPA) of 3.0 out of 4.0, or equivalent.
     

  • Students must submit the English Language Proficiency Test Score.
     

  • Students with insufficient prior background in the chosen Master’s program may be considered for conditional admission, but will be assigned undergraduate and/or graduate courses and/or specially tailored remedial courses as specified by the relevant program.
     

    Required Document List
     

    The following required documents are required:
     

  • Certified copy of Bachelor’s/Master’s degree certificate (as applicable).

  • Official transcript showing the grading scale.

  •  
  • English language proficiency score certificate (GRE, IELTS, TOEFL or EmSAT).

  • Curriculum Vitae (CV).

  • Passport-style photograph on a white background.

  • Valid passport

  • Valid UAE national ID card (for international applicants who currently reside in the UAE).

  • Statement of Purpose (500 to 1,000 words)

A master’s degree in Computer Science from Khalifa University helps open many career opportunities for future success. The field of computer science is broad, therefore, graduates should have plenty of career opportunities to choose from both locally and internationally.

This includes artificial intelligence and data analysis, systems analyst, applications analyst/developer, cyber security analyst, games designer/developer, information systems manager, and software engineer. The wide range of industries that utilize computer scientist include, information and communication technology, energy, oil and gas, healthcare, cyber security, banking/finance, robotics and autonomous systems, and transportation. The MSc in CS program at Khalifa University offers the student an excellent opportunity for interdisciplinary education, which will help them fulfill the requirement of these career paths. Graduates also go through rigorous training and research experience to enable them to pursue their studies at PhD level.

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