Doctor of Philosophy in Computer Science

First Year, Fall Semester

9 credit hours

• 1501771Advanced Data Structures and Algorithms3 credits

  Prerequisite: 1501371 or equiv.

  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.

• 1501701Mathematical and Statistical Essentials3 credits

  Prerequisite: Grad Standing

  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.

• Elective 13 credits

First Year, Spring Semester

9 credit hours

• 1501790PhD Research Seminar3 credits

  Prerequisite: Grad Standing

  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.

• Elective 23 credits
• 1501791Directed Studies3 credits

  Prerequisite: Grad Standing

  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.

Second Year, Fall Semester

9 credit hours

• 1501893PhD Qualification Exam0 credits

  Prerequisite: QE Panel Approval

  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.

• Elective 33 credits
• Elective 43 credits
• Elective 53 credits

Second Year, Spring Semester

9 credit hours

• 1501894PhD Dissertation9 credits

  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.

Third Year, Fall Semester

9 credit hours

• 1501894PhD Dissertation9 credits

Third Year, Spring Semester

9 credit hours

• 1501894PhD Dissertation9 credits

Fourth Year, Fall Semester

0 credit hours

• 1501894PhD Dissertation0 credits

Fourth Year, Spring Semester

0 credit hours

• 1501894PhD Dissertation0 credits

Elective Courses: Artificial Intelligence and Applications

Electives 1 to 5 are chosen from these groups.

• 1501731Topics in Machine Learning3 credits

  Prerequisite: 1501371 or equiv.

  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.

• 1501735Topics in Computer Vision3 credits

  Prerequisite: 1501371 or equiv.

  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.

• 1501830Topics in Artificial Intelligence3 credits

  Prerequisite: 1501440 or equiv.

  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.

• 1501730Natural Language Processing3 credits

  Prerequisite: 1501371 or equiv.

Elective Courses: Networking and Security

• 1501752Wireless Sensor Networks3 credits

  Prerequisite: Grad Standing

• 1501757Topics in Information Security3 credits

  Prerequisite: Grad Standing

• 1501753Topics in Networking3 credits

  Prerequisite: 1501352 or equiv.

Elective Courses: Information and Software

• 1501761Topics in Data Mining3 credits

  Prerequisite: 1501263 or equiv.

• 1501768Big Data and Data Analytics3 credits

  Prerequisite: 1501263 or equiv.

• 1501760Topics in Software Engineering3 credits

  Prerequisite: Grad Standing

• 1501762Topics in Database Systems3 credits

  Prerequisite: 1501263 or equiv.

• 1501861Topics in Data Analytics and Cloud Computing3 credits

  Prerequisite: Grad Standing