Degree Structure
College
Computing and Informatics
Department
Computer Science
Level
Graduate Phd
Study System
Courses and Theses
Total Credit Hours
54 Cr. Hrs.
Duration
3-5 years
Intake
Fall and Spring
Language
English
Study Mode
Full Time
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Degree Overview
The Doctor of Philosophy degree in Computer Science (PhD-CS) program's main goal is to provide advanced knowledge in the field of computer science with an in-depth research experience. The program will offer a comprehensive list of courses based on the core of computer science, research, and optimization methodologies concentrated on advanced development in computer science.
Candidates admitted to the program are expected to have completed a Master's degree in computer science or a closely related field. For the award of PhD-CS degree, candidates are required to successfully finish the course work, pass a comprehensive exam, and complete a research-based dissertation.
The PhD-CS program emphasizes proficiency in understanding fundamental and advanced topics in computer science, communicating learned knowledge with excellent oral and written skills, and taking the lead in research and development in chosen field of expertise. The candidates should demonstrate their ability to engage independently in state-of-the-art research and provide original and significant contribution in their area of specialization.
What You Will Learn
The Doctor of Philosophy degree in Computer Science (PhD-CS) program's main goal is to provide advanced knowledge in the field of computer science with in-depth research experience. The program will offer a comprehensive list of courses based on the core of computer science, research, and optimization methodologies concentrated on advanced development in computer science. The PhD-CS program emphasizes proficiency in understanding fundamental and advanced topics in computer science, communicating learned knowledge with excellent oral and written skills, and taking the lead in research and development in a chosen field of expertise.
University Requirements
a. The student must hold a master's degree with a minimum grade of "Very Good" (3.0 out of 4.0) and a bachelor's degree with a minimum grade of 2.5 out of 4.0 or equivalent from a university, college, or an institute recognized by the University of Sharjah and the Ministry of Higher Education and Scientific Research of the UAE. Students with a grade of "Good" may be accepted conditionally.
b. The Bachelor's and Master's degrees must be in a major that allows the student to pursue a doctorate graduate program. A student may be admitted, if his/her major is different from the program he/she is applying for, upon the recommendation of the Department and approval of the Council. A student who lacks necessary prerequisite courses may take remedial courses concomitantly or before the Doctorate program.
c. Meeting the TOEFL condition
College Requirements
Degree Requirements
Compulsory Courses
| Course Code | Course Title | Credit Hours | Pre-requisite |
| 1501771 | Advanced Data Structures and Algorithms | 3 | 1501371 or equiv. |
| 1501701 | Mathematical and Statistical Essentials | 3 | Grad Standing |
| 1501790 | PhD Research Seminar | 3 | Grad Standing |
| 1501791 | Directed Studies | 3 | Grad Standing |
| 1501893 | PhD Comprehensive Exam | 0 | QE Panel Approval |
| 1501894 | PhD Dissertation | 27 | - |
Elective Courses
| Group | Course Code | Course Title | Credit Hours | Pre-requisite |
| Artificial Intelligence and Applications | 1501731 | Topics in Machine Learning | 3 | 1501371 or equiv. |
| 1501735 | Topics in Computer Vision | 3 | 1501371 or equiv. | |
| 1501830 | Topics in Artificial Intelligence | 3 | 1501440 or equiv. | |
| 1501730 | Natural Language Processing | 3 | 1501371 or equiv. | |
| Networking and Security | 1501752 | Wireless Sensor Networks | 3 | Grad Standing |
| 1501757 | Topics in Information Security | 3 | Grad Standing | |
| 1501753 | Topics in Networking | 3 | 1501352 or equiv. | |
| Information and Software | 1501761 | Topics in Data Mining | 3 | 1501263 or equiv. |
| 1501768 | Big Data and Data Analytics | 3 | 1501263 or equiv. | |
| 1501760 | Topics in Software Engineering | 3 | Grad Standing | |
| 1501762 | Topics in Database Systems | 3 | 1501263 or equiv. | |
| 1501861 | Topics in Data Analytics and Cloud Computing | 3 | Grad Standing |
Course Description
| 1501771 | Advanced Data Structures and Algorithms |
| This course covers advanced data structures and algorithms to solve fundamental computing problems and shows the role of data structures in algorithm design and the use of amortized complexity analysis to determine how data structures affect performance. It covers advanced methods and techniques for designing algorithms using appropriate data structures, proving their correctness and analyzing their efficiency. Advanced Data Structures such as B-Trees, Fibonacci Heaps, and Data Structures for Disjoint Sets are discussed. Many classical network optimization algorithms, as well as newer and more efficient algorithms selected from the recent technical literature. | |
| 1501701 | Mathematical and Statistical Essentials |
| This course provides a comprehensive mathematical and statistical foundations for the program. It builds upon fundamental concepts in linear algebra, probability theory, basic statistics, and optimization. The course overviews basics and advanced topics that are frequently encountered in computer science applications. The students will learn the basic matrix operations and types, probability models and sampling distributions, statistical inference, regression and correlation analysis, supervised and unsupervised probabilistic learning. The student will also learn the principles and methods of optimization. | |
| 1501790 | PhD Research Seminar |
| This is a 3-credit hour course intended to hone students' skills and professional development for undertaking any research-oriented task. The students will sharpen their skills from knowledge exchange in a collaborative environment, such as seminars and group discussions. The students will also learn from peers to acquire, analyze, criticize, and present in a collaborative research environment. | |
| 1501791 | Directed Studies |
| This course helps the students in exploring their areas of interest or enables them to develop in-depth research in a field of interest. The students will be encouraged to target those areas of interest in which they are planning to carry out their theses. The course intends to complete and polish the knowledge of the students while allowing them to develop their critical thinking and analysis skills. The registration in this course and its topic should be approved in advance by the student's potential thesis supervisor and the PhD program coordinator. | |
| 1501893 | PhD Qualifying Exam |
| Every PhD student must pass a Comprehensive Examination designed to evaluate the breadth and depth of the student's knowledge of his or her discipline, as well as the student's scholarly potential. The comprehensive exam consists of a written exam that will be prepared, administered, and evaluated by an examination committee from the computer science department. Students taking the comprehensive exam must be in good academic standing and complete the required coursework. The Comprehensive Exam consists of three written exams covering three topics. One core topic (Advanced Data Structures and Algorithm Design), and two subjects are selected by the PhD student in consultation with his/her PhD academic advisor such as: Artificial Intelligence, Networking, Security, Software Engineering, and Data Science. | |
| 1501894 | PhD Dissertation |
| Students must undertake and complete an independent theoretical and/or practical research under the supervision of a faculty member. Students are required to submit a dissertation documenting their research and defend it in an oral examination before a committee. The dissertation work should provide the student with advanced knowledge in computer science subjects with an in-depth research experience. Students are required to produce at least one refereed publication of their work before defending the dissertation. | |
| 1501731 | Topics in Machine Learning |
| This course involves special topics in Machine Learning (ML). The course explores advanced/specialized topics in ML that are not currently offered as regular courses in the PhD in Computer Science curricula. The topics depend on the interest of the instructor and contents may vary at each offering. Main topics include regression, classification, clustering, and deep learning. In regression, we plan to cover simple and multiple models, feature selection techniques such as L1 and L2 regularization methods, and bias and variance theory. In classification, the topics include linear classifiers, logistic regression, decision trees, ensemble learning, support vector machines, and artificial neural networks. Clustering will include k-means algorithm using centroid-based and density-based types. In deep learning, we aim at covering convolutional neural networks as well as recurrent neural networks. | |
| 1501735 | Topics in Computer Vision |
| This is a special topics course. The topics course usually introduces advanced/specialized areas that are not currently offered in regular courses. The topics depend on the interest of the instructor and contents vary at each offering. Introduction to the basic and advanced concepts and techniques in computer vision. 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. | |
| 1501830 | Topics in Artificial Intelligence |
| This course involves selected topics in Artificial Intelligence (AI). The course explores advanced/specialized topics in Artificial Intelligence that are not currently offered as regular courses in the PhD in Computer Science curricula. The topics depend on the interest of the instructor and contents may vary at each offering. This advanced graduate course explores in depth several important topics in modern Artificial Intelligence. The main topic list may include intelligent agents, uninformed and informed search, adversarial search, constraint satisfaction problem, Bayesian networks, decision networks, and reinforcement learning. In addition, advanced topics will be covered from the following fields: machine learning, natural language processing, computer vision, robotics, and deep learning. | |
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