Advanced Computing
Student must complete 36 credit hours
Speciality Requirements Student must complete 24 credit hours
| Course Code | Course Name | Credit Hours | Prerequests |
|---|---|---|---|
| 483595 | Thesis | 6 |
|
| 486512 | Advanced Data Structures and Algorithms | 3 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 486541 | Analysis of Experimental Data | 3 |
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| 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 |
|
| 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 |
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| 486574 | Digital Image Processing | 3 |
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| 486591 | Special Topics | 3 |
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| Important topics in Advanced Computing. | |||
| 486592 | Special Project | 3 |
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| 487522 | Dynamic Programming | 3 |
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| 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 |
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| 487524 | Stochastic Processes (Statistical Modeling) | 3 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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. | |||