Speciality Requirements Student must complete 24 credit hours

Course Code	Course Name	Credit Hours	Prerequests
483595	Thesis	6
The Thesis course is designed to guide students through the process of conducting independent research and producing a substantial scholarly work in their chosen field of study. This culminating experience allows students to demonstrate their mastery of the knowledge, skills, and methodologies acquired throughout their academic program. The Thesis course is typically undertaken in the final year of a graduate or undergraduate program, allowing students to integrate and apply their knowledge and skills in a focused research project. Students will work closely with a faculty advisor or thesis committee throughout the process, receiving guidance and mentorship to support their research endeavors. Prerequisites: Completion of relevant coursework in the student's field of study, including research methods or capstone courses. Students should have a strong foundation in academic writing, research design, and critical thinking skills. This course is open to graduate and undergraduate students across disciplines who are pursuing a thesis or equivalent research project as part of their degree requirements. The Thesis course provides an opportunity for students to make original contributions to their field of study and prepare for future academic or professional pursuits.
486512	Advanced Data Structures and Algorithms	3
Data structures designs for effective algorithms with concentration on advanced computing, Massive data structures, Advanced algorithm design methods, NP and NP completeness, Introduction to parallel algorithms and Heuristic algorithms.
486572	Computer Modeling and Simulation	3
The course covers topics includes, types of simulators, constructing computer models, discrete modeling, and agent based modeling, continuous modeling, random number generation, finite element and finite difference based modeling, and verification and validation.
486577	High Performance Computing	3
Introduction to parallel computing, parallel processing and quantum computing. Parallel programming and parallel algorithm design, Parallel program debugging and performance computing, load balancing and parallel computing design.
486599	Seminar in Advanced Computing	0
487503	Numerical Partial Differential Equations	3
This course covers the main methods of solving partial differential equations. The course covers topics includes, finite difference approximation, hyperbolic equations, parabolic equations, elliptic equations, finite element approximation, and convergence and error estimation.
487521	Computational Linear Algebra	3
Review of vectors, matrices and linear equations, review of eigenvalues and eigenvectors, direct computational methods for solving linear equations , iterative computational methods for solving linear equations: Jacobi, Gauss-Seidel and SOR methods, convergence and divergence, computational methods for solving eigenvalue problems: power and inverse power methods, Sturm sequences, similarity transformations, LR and QR algorithms.
487525	Advanced Linear Programming	3
The course covers advanced topics in linear programming includes: vector analysis, simplex methods, duality and sensitivity analysis, special simplex forms, transportation and assignment problems, game theory, revised simplex methods, parametric linear programming, and networks.

Speciality Optional Requirements Student must complete 12 credit hours

Course Code	Course Name	Credit Hours	Prerequests
429531	Applied Computational Statistics	3
This course consists of a survey of current statistical software, numerical methods of statistical computations, non-linear optimisation, statistical simulation and recent in computer?intensive statistical methods.
429533	Iterative Methods	3
This course looks at iterative methods for non-linear equations, systems of linear equations, system of nonlinear equations and conjugate gradient methods.
486374	Digital Image Processing	3
486541	Analysis of Experimental Data	3
This course introduces students to experimental design and analysis of data from experiments. The course provides knowledge on how to plan, design, conduct experiments, and analyze the data to make conclusions. Topics covered in this course includes, analysis of variance, randomized block design, Latin-square design, factorial design, design with random factors and nested design.
486553	Principles of Data Management	3
Modern scientific and engineering applications and instruments produce large amount of data that require advanced algorithms to manage it and access it. This course covers the fundamental principles of large-scale data management. The course covers topics related to data representation, organization, access, storage, and processing. This will include topics such as metadata, data storage systems, self-descriptive data representations, semi-structured data models, and large-scale data analysis.
486564	Information Retrieval Systems	3
This course studies the theory, design, and implementation of Information Retrieval, includes statistical characteristics of unstructured information, such as documents, representation of information needs and documents, several important retrieval models (Boolean, vector space, probabilistic, inference net, language modeling) and experimental evaluation. as well Semantic and ontology languages, description logic, reasoning and rule languages; and agent communication languages, protocols and standards.
486567	Data Mining	3	486512 487524
Data Mining studies algorithms and computational paradigms that allow computers to find patterns and regularities in large scale data, perform prediction and forecasting, and generally improve their performance through interaction with data. The knowledge discovery process includes data selection, cleaning, coding, using different statistical and machine learning techniques, and visualization of the generated structures.
486573	Artificial Intelligence	3
This course provides an in-depth exploration of the field of Artificial Intelligence (AI), covering its fundamental principles, techniques, and applications. Through a combination of lectures, discussions, hands-on projects, and case studies, students will gain a comprehensive understanding of the core concepts and methodologies that underpin AI. Throughout the course, students will have the opportunity to apply theoretical concepts to real-world problems through hands-on programming assignments and projects. By the end of the course, students will have developed the knowledge and skills necessary to design, implement, and evaluate AI systems across various domains. Prerequisites: Basic programming skills in a high-level language (e.g., Python), familiarity with fundamental concepts in mathematics (e.g., calculus, linear algebra, probability), and a strong desire to explore and understand the principles of Artificial Intelligence. This course is suitable for undergraduate and graduate students majoring in Computer Science, Engineering, Mathematics, or related fields, as well as professionals seeking to enhance their knowledge and skills in Artificial Intelligence.
486574	Digital Image Processing	3
Image Processing is a fundamental field at the intersection of computer science, engineering, and mathematics, focusing on the manipulation, analysis, and interpretation of digital images. This course provides a comprehensive overview of image processing techniques, algorithms, and applications, equipping students with the knowledge and skills necessary to work with images in various domains. Throughout the course, students will engage in hands-on programming assignments and projects to implement and experiment with image processing algorithms using software tools such as MATLAB, Python (with libraries like OpenCV), or specialized image processing software. Emphasis will be placed on both theoretical understanding and practical application, with real-world examples and case studies illustrating the relevance of image processing techniques in various industries and research fields. Prerequisites: Basic knowledge of linear algebra, calculus, and programming (e.g., MATLAB, Python). Familiarity with fundamental concepts in signal processing and computer vision is helpful but not required. This course is suitable for undergraduate and graduate students majoring in Computer Science, Electrical Engineering, Biomedical Engineering, or related disciplines, as well as professionals seeking to enhance their skills in image processing and analysis.
486591	Special Topics	3
Important topics in Advanced Computing.
486592	Special Project	3
487522	Dynamic Programming	3
Spanning trees, route, maximum flow, transportation and transshipment problems, problems in multidimensional, economic decisions, multistage problem solving, and decomposition and recursive equations for final state and initial-final state optimization.
487523	Advanced Operation Research	3	487525
This course delves into advanced topics in Operations Research (OR), a discipline that utilizes mathematical modeling, optimization techniques, and analytical methods to aid decision-making in complex systems. Building upon foundational concepts in OR, this course offers an in-depth exploration of advanced methodologies and applications across diverse domains. The course emphasizes both theoretical understanding and practical application of advanced OR techniques through case studies, modeling exercises, and hands-on projects. Students will have the opportunity to work with industry-standard software tools and develop skills in formulating, solving, and analyzing complex optimization problems. Prerequisites: Prior coursework in Operations Research or Optimization, familiarity with linear algebra, probability theory, and mathematical modeling. Proficiency in programming (e.g., Python, MATLAB) is recommended but not mandatory. This course is designed for graduate students and advanced undergraduate students majoring in Operations Research, Industrial Engineering, Applied Mathematics, or related fields. Professionals seeking to deepen their expertise in optimization and decision analysis will also benefit from this course.
487524	Stochastic Processes (Statistical Modeling)	3
The course introduces students with the theory and algorithms for modeling complex patterns in high dimensional spaces. The course covers two classes of statistical modeling: descriptive models (Markov random field and Gibbs distributions), and generative models and cover topics include: mathematical modeling, random walk, markov chain, monte-carlo modeling, and classification of states, communicating, periodicity, stopping times, and ergodic systems.
487526	Numerical Optimization	3
This course introduces students to numerical optimization methods for constrained and unconstrained non-linear optimization. The course also introduces students to stochastic global optimization (e.g. simulated annealing and genetic algorithms) and neural network methods. The course combines both theoretical and practical aspects of optimization through the application and comparisons of different optimization methods on practical problems using computer.
487535	Approximation Theory	3	487521
This course introduces students to Approximation Theory. The course covers topics include, normed linear spaces, convexity, stability, stationary, stiffness, existence and uniqueness of best approximations, chebychev approximation by polynomials and other related families, and least squares approximation and splines.
487573	Graph Theory	3	486512
Survey of several of the main ideas of general graph theory with applications to network theory, oriented and non-oriented linear graphs, spanning trees, branches and connectivity, accessibility, planar graphs, networks and flows, matching and applications.
487575	Fuzzy Systems	3
Fuzzy sets, fuzzy numbers, ranking of fuzzy numbers, fuzzy difference equations, fuzzy matrices, fuzzy vector spaces, decision ? making with fuzzy preference relation, fuzzy relation equation and fuzzy logic. The course will cover the application of fuzzy systems in selected areas through case studies and paper reviews.