College Algebra (MATH097)

This course is designed for students who intend to take admit in college of Law without fulfilling their college mathematics requirement. The course contains all the elements that are necessary for success in their courses. Algebraic skills will be developed throughout the course. Intensive treatment of algebra will be covered. Topics include problem solving with equations, inequalities, functions and their graphs, equations involving polynomial and rational functions, exponential and logarithmic functions. Finally, topics include linear system and basic operation of matrices.

Credit Hours : 0

Course Learning Outcomes

1. Solve different types of equations: quadratic, cubic, rational, exponential, and logarithmic equations using different methods.
2. Identify algebraically and graphically various properties of functions.
3. Describe basic algebraic operations on functions.

Pre-Calculus for CBE and CAVM (MATH098)

This course is designed for students who intend to take calculus for students from colleges of CBE and CAVM in their undergraduate studies and have joined the university without fulfilling their college mathematics requirement. The course contains all the elements that are necessary for success in calculus and in other undergraduate mathematics courses. Algebraic and analytic skills will be developed throughout the course. Intensive treatment of algebra, geometry and trigonometry will be covered. Topics include problem solving with equations, inequalities, distance, circles and triangles, functions and their graphs, equations involving polynomial and rational functions, exponential and logarithmic functions, trigonometric functions, and their inverses. Finally, topics include linear system and basic operation of matrices.

Credit Hours : 0

Course Learning Outcomes

1. Solve different types of equations: quadratic, cubic, rational, exponential, logarithmic, and trigonometric equations using different methods.
2. Identify algebraically and graphically various properties of functions.
3. Describe basic algebraic operations on functions.
4. Write the decomposition of a rational function into simple fractions.
5. Evaluate trigonometric functions at a given angle and verify trigonometric identities.
6. Perform basic matrix operations and solve 2 × 2 linear systems of equations.

Pre-Calculus for COS, COE, and CIT (MATH099)

This course is designed for students who intend to take calculus I for Science or Calculus I for Engineering course in their undergraduate studies and have joined the university without fulfilling their college mathematics requirement. The course contains all the elements that are necessary for success in calculus and in other undergraduate mathematics courses. Algebraic and analytic skills will be developed throughout the course. Intensive treatment of algebra, geometry and trigonometry will be covered. Topics include problem solving with equations, inequalities, distance, circles and triangles, functions and their graphs, equations involving polynomial and rational functions, exponential and logarithmic functions, trigonometric functions, and their inverses. Finally, topics include linear system and basic operation of matrices.

Credit Hours : 0

Course Learning Outcomes

1. Solve different types of equations: quadratic, cubic, rational, exponential, logarithmic, and trigonometric equations using different methods.
2. Identify algebraically and graphically various properties of functions.
3. Describe basic algebraic operations on functions.
4. Write the decomposition of a rational function into simple fractions.
5. Evaluate trigonometric functions at a given angle and verify trigonometric identities.
6. Perform basic matrix operations and solve 2 × 2 linear systems of equations.

Calculus I (MATH105)

Elementary functions, limits, continuity, limits involving infinity, tangent lines, derivative of elementary functions, differentiation rules, chain rule, implicit differentiation, linear approximation, l'Hospital rule. Graph sketching (extrema, intervals of monotonicity, concavity), optimization. Antiderivatives, definite integrals, Fundamental Theorem of Calculus, integration by substitution, area between curves, improper integrals.

Credit Hours : 3

Course Learning Outcomes

1. Explain some important concepts of calculus (such as limit, continuity, derivative and integral).
2. Compute limits, derivatives, linear approximations, and integrals using various techniques.
3. Apply calculus to geometry and to real world problems (Such as graph sketching, optimization, related rates, area computation).
4. Use technology to investigate limits, graphs, and integrals.
5. Justify some general results in single-variable calculus from a theoretical point of view.

Calculus II (MATH110)

Integration techniques (by parts, by use of trigonometry, by partial fractions), volume and area of solids of revolution, arc length. Parametric curves: velocity vector, enclosed area, arc length. Curves in polar coordinates: enclosed area, conic sections. Sequences, series, convergence tests, alternating series, absolute convergence, power series, Taylor series, Fourier series.

Credit Hours : 3

Prerequisites

• MATH105 with a minimum grade D

Course Learning Outcomes

1. Explain the concepts of sequences, series, polar coordinates, parametric equations.
2. Compute integrals and Taylor series using various techniques.
3. Justify the convergence of sequences and series.
4. Apply Calculus to real world problems such as area, volume, and arc-length.
5. Communicate solutions of problems with peers and written assignments.

Calculus for Business & Economics (MATH115)

This course introduces the concepts of differential and integral calculus useful to students in business, economics. Among the topics studied are: curve sketching for some functions relevant to business and economics applications, derivatives and techniques of differentiation, exponential growth, anti-derivatives and methods of integration, definite and indefinite integrals with applications. The course also covers topics on partial derivatives and matrices, in addition to many applications in Business and Economics.

Credit Hours : 3

Course Learning Outcomes

1. Identify basic properties of several elementary functions
2. Compute derivatives of several elementary functions including multivariate functions
3. Evaluate different types of definite and indefinite integrals
4. Perform basic matrix operations
5. Apply mathematical models and tools to various business and economics problems

Contemporary Applications of Math (MATH120)

Problem solving, fair divisions, Mathematics of Apportionment, Euler circuits, network, scheduling methods, population growth, symmetry, fractal geometry.

Credit Hours : 3

Course Learning Outcomes

1. Use quantitative methods to analyze real-world problems involving social and management issues.
2. Apply the concepts and methods of graphing theory to solve problems in scheduling, networking and routing.
3. Analyze appearances of patterns and symmetry in nature.

Calculus I for Engineering (MATH130)

Differential calculus of functions of one variable: functions of one variable, techniques of differentiation, derivatives of trigonometric, exponential, and logarithmic functions, chain rule, implicit differentiation, maximum and minimum values, increasing, decreasing and concave functions, inverse trigonometric functions, hyperbolic functions, some engineering applications. Integral calculus of functions of one variable: definite and indefinite integrals, techniques of integration (integration by substitution, integration by trigonometric substitutions, integration by parts, integration by partial fractions), applications of definite integrals in geometry, some engineering applications.

Credit Hours : 3

Course Learning Outcomes

1. Compute derivatives of various types of functions
2. Apply the derivative to model various engineering problems
3. Analyze the properties of various types of functions and theirs graphs
4. Classify integration techniques and integrate different types of functions
5. Use the integration to compute areas between curves, volumes of solids, and volumes of revolutions.

Calculus II for Engineering (MATH135)

Differential calculus of functions of several variables: vectors, vector valued functions, functions of several variables, partial derivatives, chain rule, gradient and directional derivatives, extrema of functions of several variables. Quadratic surfaces. Vector fields and line integrals, double integrals in Cartesian and polar coordinates, triple integrals in Cartesian, cylindrical and spherical coordinates.

Credit Hours : 3

Prerequisites

• MATH130 with a minimum grade D

Course Learning Outcomes

1. Apply The Properties Of Vectors, Lines And Planes.
2. Compute Partial Derivatives, Rate Of Change, And Extrema Of Functions Of Several Variables.
3. Formulate The Concepts Of Vector-Valued Functions, Vector Fields And Line Integrals.
4. Manipulate Multiple Integrals To Calculate Areas, Volumes, And Center Of Mass For Different Configurations.
5. Use Critical Thinking For Analyzing Engineering Problems.

Linear Algebra I (MATH140)

Systems of linear equations, matrices and determinants. Vector spaces, inner product spaces. Matrix representations of linear operators. Eigenvalues, eigenvectors, and Cayley-Hamilton Theorem.

Credit Hours : 3

Course Learning Outcomes

1. Explain the main concepts of linear algebra (matrices, determinants, vector spaces, and linear transformations).
2. Solve systems of linear equations and problems related to linear transformations.
3. Use computational software to determine solutions of systems of linear equations, and the eigenvalues and eigenvectors of a matrix.
4. Communicate concepts and results in linear algebra to their peers.

Set Theory and Logic (MATH205)

Compound and simple propositions, truth table, quantifiers, propositional calculus, methods of proof. Sets and operations on it. Cartesian products, relations, equivalence relation, order relation. Functions. Cardinality.

Credit Hours : 3

Prerequisites

• MATH110 with a minimum grade D
• MATH140 with a minimum grade D

Course Learning Outcomes

1. Demonstrate ability to integrate knowledge and idea in a coherent manner.
2. Justify procedures of abstract proofs by applying Logic.
3. Develop counterexamples to claim assertions and to make interpretation of statements.
4. Recognize procedures of abstract proofs.

Calculus III (MATH210)

Euclidean space: dot product, cross product, lines, planes, surfaces. Parametric curves in space. Functions of several variables: limits, continuity, partial derivatives, tangent plane, linear approximation, chain rule, gradient, directional derivative, extrema, Lagrange multipliers. Double integrals, applications (area, volume, center of mass), change to polar coordinates. Triple integrals, change to cylindrical and spherical coordinates. Vector fields, line integrals, conservative fields, Green's theorem.

Credit Hours : 3

Prerequisites

• MATH110 with a minimum grade D
• MATH140 with a minimum grade D

Course Learning Outcomes

1. Explain important concepts of multi-dimensional Calculus (such as: Euclidean space, partial derivatives, multiple integrals, vector fields)
2. Solve problems related to differential and integral Calculus in multi-dimensional spaces.
3. Apply Calculus to real world problems (e.g., optimization in several variables, area and volume computations).
4. Use technology to visualize multidimensional surfaces.
5. Discuss solutions of multi-variable Calculus problems with their peers.
6. Work in a group in Calculus peer-tutoring sessions.

Introduction to Analysis (MATH215)

Properties of R. Completeness of the line, supremum and infimum, Cantor's nested intervals theorem. Sequences, limits and their properties, monotone sequences, Bolzano-Weierstrass Theorem, Cauchy criterion, properly divergent sequences. Series, absolute and conditional convergence, tests of convergence. Topological properties of R, Metric spaces and general topology.

Credit Hours : 3

Prerequisites

• MATH205 with a minimum grade D or CENG202

Course Learning Outcomes

1. Examine the properties of the real numbers.
2. Solve problems related to convergence of sequences and series of real numbers.
3. Explain basic concepts and results of Analysis to their peers.
4. Describe the topological properties of metric spaces.

Applied Linear Algebra (MATH240)

This course focuses on the basic concepts of tensor analysis and various matrix decompositions and factorizations with an emphasis on concrete calculations and applications. Specific topics to be covered include Gram-Schmidt diagonalization, LU, QR, Singular-Value Decomposition, and Eigen-Decomposition. The course also introduces students to orthogonal spaces and projection operators. Computer algebra systems are used to solve problems in different areas related to Linear Algebra.

Credit Hours : 3

Prerequisites

• MATH140 with a minimum grade D

Course Learning Outcomes

1. Explain the main concepts of applied Linear Algebra (Gram-Schmidt diagonalization, various matrix decomposition and factorization, tensor analysis, projection operators and orthogonal systems).
2. Understand the computational methods and algorithms behind the topics in applied linear algebra
3. Use computational software to solve problems in data science as an application of linear algebra
4. Communicate concepts, results in applied Linear Algebra to their peers.

Number Theory (MATH246)

Divisibility, Euclidean algorithm, prime numbers, the Fundamental Theorem of Arithmetic, the Sieve of Eratosthenes. Congruence, Diophantine equations, Chinese Remainder Theorem. Fermat's theorem, Wilson's theorem, Euler's theorem.

Credit Hours : 3

Prerequisites

• MATH205 with a minimum grade D

Course Learning Outcomes

1. Explain various linear programming methods such as geometric method, simplex method, duality method.
2. Formulate problems from various fields in the language of linear programming.
3. Discuss the conditions of validity of several linear programming methods.
4. Solve linear programming problems by various methods.

Foundation of Geometry (MATH260)

Euclid's postulates and plane geometry. Von-Neumann postulates. The parallel postulate. Affine geometry and geometry on the sphere. Projective and hyperbolic geometries. Klein-Beltrami and Poincare models of the plane. Pappus and Desargues theorems. Transformations: automorphisms, motions, similarities, and congruence.

Credit Hours : 3

Prerequisites

• MATH135 with a minimum grade D or MATH110 with a minimum grade D
• MATH140 with a minimum grade D

Course Learning Outcomes

1. Communicate Concepts And Results In Geometry To Their Peers.
2. Construct Scientifically Geometric Figures With A Ruler And A Compass.
3. Establish Geometric Results From Deductive Reasoning.
4. Explain Non-Euclidean Geometries, Including Hyperbolic Geometry.
5. Obtain Results In Non-Euclidean Geometries From Models In Euclidean Geometry.
6. State The Theory And Practice Of Traditional Euclidean Geometry.

Ordinary Differential Equations (MATH275)

First order differential equations: examples, separable equations, homogeneous and exact equations, integrating factor and Bernoulli's equation, linear equations, initial value problems. Higher order differential equations: linear equations, linear independence and Wronskian matrices, existence and uniqueness of solutions. Particular solutions: the method of undetermined coefficients, the method of variation of parameters. Laplace transforms and initial value problems. Series solution of differential equations. System of equations and their matrix form.

Credit Hours : 3

Prerequisites

• (MATH110 with a minimum grade D or MATH135 with a minimum grade D)
• (MATH140 with a minimum grade D)

Course Learning Outcomes

1. Apply differential equations to model real life problems.
2. Classify the different types of ordinary differential equations
3. Justify some statements in ordinary differential equations (for instance, about the existence and uniqueness of solutions).
4. Solve first and second order differential equations using a variety of techniques

Computational Mathematics (MATH290)

This course is computational in nature. It introduces various numerical techniques in mathematics. It focuses on numerical approximation of solution to different problems such as approximation of functions and solutions of differential equations. The course relies heavily on coding the presented techniques. The course also introduces the Laplace and Fourier transforms and their applications in differential equations.

Credit Hours : 3

Prerequisites

• MATH110 with a minimum grade D
• MATH140 with a minimum grade D

Course Learning Outcomes

1. Understand error analysis
2. Solve numerically nonlinear equations and systems of equations
3. Interpolate data
4. Apply various numerical techniques for solving different types of differential equations under different conditions.

Mathematics For Teachers I (MATH305)

Introduction to mathematical logic, sets, operation on sets, the set of natural numbers, the set of integers, the set of rational numbers, graphical representation of numbers, decimal representation of numbers, other bases, divisibility, solution of arithmetic problems, applications.

Credit Hours : 3

Course Learning Outcomes

1. Analyze sets and set operations.
2. Identify early (Egyptian, Chinese, Babylonian, Roman) and modern numeration systems.
3. Manipulate different base number systems.
4. Operate with natural, integer, rational and real numbers.
5. Recognize logical statements and quantifiers.
6. Solve problems using inductive and deductive reasoning.

Real Analysis (MATH310)

Functions, limits of functions, limits involving infinity, continuity, uniform continuity, Extreme Value Theorem, Intermediate Value Theorem, monotone and inverse functions. Differentiation, Mean Value theorem, L'Hospital's rule, Taylor's theorem. Riemann integral, the Fundamental Theorem of Calculus

Credit Hours : 3

Prerequisites

• MATH215 with a minimum grade D

Course Learning Outcomes

1. Explain the notions and results of single-variable Calculus in a theoretical way.
2. Solve problems in Real Analysis in a deductive way.
3. Analyze statements in Analysis from the definitions and theorems.
4. Illustrate concepts and results of Real Analysis to their peers.
5. Reproduce from the literature information for proving a result in Analysis.

Advanced Calculus (MATH313)

Vector-valued functions of n variables: limits, continuity, Jacobian matrix, differentiability, general chain rule. Implicit Function Theorem for many variables. Scalar-valued functions of n variables: multidimensional Taylor series, Hessian, optimization, constrained optimization. Multiple integrals: Jacobian, change of variables formula, improper integrals. Parametric surfaces: tangent plane, area, integrals over a surface. Vector Calculus: vector fields, divergence, curl, surface integrals of a vector field, Stokes' and Gauss' theorems.

Credit Hours : 3

Prerequisites

• MATH210 with a minimum grade D

Course Learning Outcomes

1. Explain important concepts and results of vector Calculus (such as Jacobian matrix, differentiability, vector fields, surface integrals, curl, divergence, Stokes' theorem).
2. Evaluate derivatives and integrals involving scalar or vector fields.
3. Apply Calculus to real life problems.

Complex Analysis I (MATH315)

Complex numbers: properties and representations. Complex functions: limits, continuity, and the derivative. Analytic functions: Cauchy - Riemann equations, harmonic functions, elementary analytic functions. Integration in the complex plane: complex line integrals, Cauchy integral theorem, Morera's theorem, Cauchy integral formula; Maximum principle. Liouville's theorem and the fundamental theorem of algebra.

Credit Hours : 3

Prerequisites

• MATH210 with a minimum grade D
• MATH205 with a minimum grade D or CENG202

Course Learning Outcomes

1. Describe essential concepts in complex Analysis, such as: function of a complex variable, their limits, continuity, derivatives, and integrals.
2. Evaluate elementary complex functions and multi-functions.
3. Compute derivatives, harmonic conjugates, and contour integrals.
4. Deduce theoretical results from Cauchy’s theorem (such as Morera’stheorem, Liouville’s theorem, Fundamental theorem of algebra, Maximum modulus principle).
5. Communicate concepts and results of Complex Analysis to their peers.

Numerical Analysis I (MATH320)

Error analysis: solutions of non-linear equations in one variable, bisection, fixed point, and false position methods, Newton and secant methods; Solution of a system of linear equations: Gaussian elimination method, Cholesky factorization method. Iterative methods: Interpolation: Lagrange, divided differences, forward, backward, and central methods. Numerical differentiation, two, three and five point formulas. Numerical integration, trapezoidal, Simpson's rules and composite quadrature.

Credit Hours : 3

Prerequisites

• MATH205 with a minimum grade D or CENG202

Course Learning Outcomes

1. Analyse the theoretical and practical aspects of numerical techniques.
2. Solve linear and non-linear equations numerically.
3. Use Mathematica software to solve numerically mathematical problems.
4. Examine the use of numerical techniques for solving problems in applied mathematics.

Linear Programming (MATH321)

General Linear Programming Problem. Geometric method. Simplex method. Revised Simplex method. Computer implementations. Duality. Parametric linear programming. Interior point methods. Applications including: transportation problem, inventory problems, blending problems and game theory.

Credit Hours : 3

Prerequisites

• MATH205 with a minimum grade D

Course Learning Outcomes

1. Explain various linear programming methods such as geometric method, simplex method, duality method.
2. Formulate problems from various fields in the language of linear programming.
3. Discuss the conditions of validity of several linear programming methods.
4. Solve linear programming problems by various methods.

Mathematics for Teachers II (MATH335)

Geometrical figures in plane and space and their properties. Areas and volumes of geometrical figures; unitary and non-unitary linear transformations and their properties. Ratio, proportion, percentage and their practical applications. The geometric problem: construction and solutions methods.

Credit Hours : 3

Prerequisites

• MATH305 with a minimum grade D

Course Learning Outcomes

1. Compute perimeters, areas and volumes of some two and three-dimensional geometrical shapes.
2. Explain some fundamental ideas of algebra (variables, expressions, equations, inequalities).
3. Manipulate data and different units measures.
4. Operate with ratio, proportion and percentage.
5. Solve systems of linear equations.

Abstract Algebra 1 (MATH340)

Groups: examples, subgroups, cyclic subgroups; cosets and Lagrange's theorem; Cyclic groups and permutation groups. Normal subgroups, quotient groups; homomorphisms and isomorphisms; Direct products of groups. Rings: examples, sub rings, ideals, quotient rings, integral domains, Fields. Ring homomorphisms and isomorphisms.

Credit Hours : 3

Prerequisites

• MATH246 with a minimum grade D

Course Learning Outcomes

1. Classify Algebraic Structures.
2. Communicate Concepts And Results Of Abstract Algebra To Their Peers.
3. Explain Basic Concepts Related To Various Algebraic Structures (Such As Groups, Rings And Fields).
4. Retrieve From The Literature Information For Solving A Problem In Abstract Algebra.
5. Solve An Algebraic Problem In A Deductive Way.

Linear Algebra II (MATH341)

Linear Transformations: Isomorphisms of vector spaces, representation by matrices, and change of basis. Eigenvalues and eigenvectors: diagonalization and triangularization of linear operators. Inner product spaces: Orthogonalization and Rieze representation theorem. Self-adjoint operators: the Spectral theorem, Bilinear and quadratic forms.

Credit Hours : 3

Prerequisites

• MATH205 with a minimum grade D

Course Learning Outcomes

1. Communicate Concepts And Results Of Linear Algebra To Their Peers.
2. Demonstrate Diagonalizability Of Linear Operators And Matrices From The Spectral Theorem.
3. Explain Concepts Of Linear Algebra Such As: Linear Transformations And Operators, Vector Spaces, And Inner Product Spaces.
4. Find The Appropriate References For Solving Other Problems In Linear Algebra.
5. Show Ability To Diagonalize, Triangularize, And Orthogonalize Linear Operators.

Graph Theory (MATH342)

Definition of a graph. Examples, paths and cycles: Eulerian and Hamiltonian graphs. Application to shortest path and Chinese postman problems, trees, applications, including enumeration of molecules, planar graphs, graphs on other surfaces, dual graphs. Coloring maps, edges, vertices. Digraphs, Markov chains, Hall's marriage theorem and applications. Network flows.

Credit Hours : 3

Prerequisites

• MATH205 or CENG202

Course Learning Outcomes

1. Apply A Selection Of Algorithms To Solve Practical Problems Involving Discrete Quantities.
2. Compute Graph Invariants Such As Chromatic Number, Chromatic Index And Chromatic Polynomial Of Some Examples Of Graphs.
3. Explain Basic Graph Theory Concepts To Their Peers.
4. Summarize The Basic Concepts In Graph Theory, Including Properties And Characterization Of Bipartite Graphs, Trees, Eulerian And Hamiltonian Graphs And Digraphs.
5. Survey In The Literature Beyond The Textbook.

Introduction to Cryptography and Coding Theory (MATH344)

This course introduces students to the principles and practices which are required for secure communication: cryptography and cryptanalysis, including authentication and digital signatures. Mathematical tools and algorithms are used to build and analyze secure cryptographic systems. Basic notions of coding theory will be also covered

Credit Hours : 3

Prerequisites

• MATH246 with a minimum grade D

Course Learning Outcomes

1. Analyze Error-Correcting Codes.
2. Apply Encryption And Signing Techniques.
3. Develop Work Attitude In A Team On Related Projects.
4. Implement Fundamental Concepts Of Cryptography And Coding Theory.

Optimization Methods (MATH350)

The course will cover the following optimization methods and techniques: Least Squares, Lagrange Multipliers, Newton’s method, Interior point method, Secant method, Quasi-Newton method. We will study the theory and implementation of these methods. The applications of these techniques in various Data Science problems from areas such as statistics, machine learning, engineering, and finance will be discussed. The practical component of this course can comprise computing laboratory work using suitable computer software like Python. This will reinforce the theoretical analysis of problems, methods, and implementation.

Credit Hours : 3

Prerequisites

• MATH110 with a minimum grade D

Course Learning Outcomes

1. Solve different types of optimization problems
2. Model real-life problems as optimization problems
3. Apply appropriate solution methods to a given optimization problem
4. Use software to solve optimization problems
5. Communicate mathematical concepts, results in optimizations to their peers

Mathematics for Machine Learning (MATH360)

This course provides a rigorous mathematical basis on machine learning algorithms. A fundamental learning theory for fundamental learnability of a machine is introduced. The main focus of the course is to mathematically investigate an optimal functional relationship between training data sets and a desired outcome for future prediction with a new data set via machine learning algorithms. Optimization techniques based on least square methods, Lagrange multiplier, and gradient descent methods are used in search for the functional relationship in using machine learning algorithms for the trade-off between training and prediction.

Credit Hours : 3

Prerequisites

• MATH110 with a minimum grade D

Course Learning Outcomes

1. Understand the basic learning theory for machine learning algorithms
2. Explain mathematical fundamentals of various machine learning algorithms
3. Use software to solve problems in data science as a practice of the machine learning algorithms
4. Communicate mathematical concepts, results in machine learning algorithms to their peers

Partial Differential Equations (MATH372)

Definitions and concepts: general and particular solutions. Elimination of arbitrary constants and functions. First order equations (the method of characteristics). Second order equations: classifications (hyperbolic, elliptic, parabolic), the normal form. Boundary value problems: the heat equation, the wave equation, Laplace equation. Methods of solutions: separation of variables, the Fourier and Laplace transforms.

Credit Hours : 3

Prerequisites

• MATH210 with a minimum grade D
• MATH275 with a minimum grade D

Course Learning Outcomes

1. Apply The Mathematical Software Mathematica To Obtain Analytical Solutions Of Pde’S.
2. Classify Different Types Of Partial Differential Equations.
3. Solve First Order Linear And Nonlinear Pde’S Using Several Techniques.
4. Solve Special Types Of Second Order Pde’S, The Wave, The Heat And The Laplace Equations Using Variety Of Techniques.
5. Use Boundary Value Problems To Model Real-Life Problems.

Dynamical Systems and Applications (MATH374)

One dimensional discrete dynamical systems. Steady states, stability, periodic points. Chaos. Lyapunov exponents. Symbolic dynamics. 2-dimensional systems. Mandelbort set. Fractals. Applications in ecology population growth, Predator-prey and competition models. Applications in medicine fractal structure of the lung, heart rat variability.

Credit Hours : 3

Prerequisites

• MATH210 with a minimum grade D
• MATH275 with a minimum grade D

Course Learning Outcomes

1. Analyze The Solution Of Different Types Of Dynamical Systems In Terms Of Steady States, Stability, And Periodic Points.
2. Communicate Findings Of A Model In The Language Of The Corresponding Problem.
3. Deduce Theoretical Results From Lyapunov Exponents.
4. Describe The Dynamic Of Discrete And Continuous Systems.
5. Develop Mathematical Models Related To Ecology Population Growth, Predator-Prey And Competition Models, Medicine Fractal Structure Of The Lung And Heart Rat Variability.
6. Use Technology To Obtain Relevant Solutions Of Dynamical Systems.

Financial Mathematics (MATH391)

Introduction to the concepts of financial markets and products. Financial derivatives, options, futures and forwards. Pricing, hedging and no arbitrage concepts. The Binomial model. Introduction to stochastic calculus, Stochastic processes, Markov property, martingales. Brownian motion, stochastic integration, stochastic differential equations, Ito's Lemma. Black and Scholes formula, delta hedging. Numerical Methods for finance, Finite Difference Methods, Monte Carlo simulation. Optional topics: Value at Risk, Greeks, Implied volatility, implementation of pricing formulas in VBA for Excel, interest rate models, exotic options, path dependent options, Asian options.

Credit Hours : 3

Prerequisites

• MATH275 with a minimum grade D
• STAT230 with a minimum grade D

Course Learning Outcomes

1. Collect Material In Financial Mathematics Beyond The Contents Of The Textbook.
2. Contribute—As Part Of A Team—To A Project On Modeling And Solving A Financial Derivative Problem.
3. Describe The Practical Meanings Of Different Financial Products And Markets And Basic Concepts Of Stochastic Calculus.
4. Justify Some Statements In Financial Mathematics By Critical And Deductive Thinking.
5. Solve Different Types Of Stochastic Differential Equations.
6. Use Binomial Trees Or Stochastic Differential Equations To Model Financial Asset Price Trajectories.

Complex Analysis II (MATH413)

Sequences and series of complex numbers, Power series, Taylor and Laurent expansions, differentiation and integration of power series, application of the Cauchy theorem: Residue theorem, evaluation of improper real integrals, conformal mappings, mapping by elementary functions.

Credit Hours : 3

Prerequisites

• MATH315 with a minimum grade D

Course Learning Outcomes

1. Apply techniques of complex analysis to solve various problems.
2. Describe the fundamental concepts of complex analysis.
3. Explain results in complex analysis to their peers.
4. Survey the literature beyond the textbook.

Numerical Analysis II (MATH422)

Approximation theory: Orthogonal and Chebyschev polynomials, rational and trigonometric polynomials, multiple integrals, initial value problems: Taylor's methods, multistep and Runge-Kutta methods, boundary value problems: shooting, finite difference and Rayleigh-Ritz methods.

Credit Hours : 3

Prerequisites

• MATH320 with a minimum grade D
• MATH275 with a minimum grade D

Course Learning Outcomes

1. Solve problems on some important concepts of Numerical Analysis (such as least squares approximation, double integrals, convergence, and stability).
2. Formulate some numerical method for approximating the initial value problems, ordinary and partial boundary value problems.
3. Show theorems in numerical analysis using different types of logical proofs.
4. Apply Numerical Analysis to real world problems (such as heat equations and wave equations).
5. Write Mathematica codes to investigate approximations, integrals, and numerical techniques for solving initial and boundary value problems.
6. Work effectively in team on a numerical analysis project.

Abstract Algebra 2 (MATH443)

Rings: introduction to rings properties and subrings; Integral Domain (ID), fields and characteristic of a ring; Ideals and factor rings; ring homomorphisms, polynomial rings and factorization of polynomials; Divisibility in ID and Unique Factorization Domain (UFD). Fields: the Fundamental Theorem of Fields; Splitting Field; Zeroes of Irreducible polynomial; Algebraic extension of Fields; Finite Fields; Introduction to Galois Theory.

Credit Hours : 3

Prerequisites

• MATH340 with a minimum grade D

Course Learning Outcomes

1. Communicate concepts and results of abstract algebra to their peers.
2. Create counter examples to disprove algebraic statements.
3. Determine the zeroes and splitting fields of polynomials, the subfields of (finite) fields, and the galois group of various field extensions.
4. Discover The Fundamental Concepts Of Abstract Algebra: Rings, Integral Domains, Ring Homomorphisms, Ideals, Factor Rings, And Fields.
5. Explore in the literature, beyond the textbook, topics in abstract algebra.

Introduction to Topology (MATH462)

Topological spaces, Bases and sub-bases, subspaces, finite product spaces, continuous maps, homomorphisms, Hausdorff spaces, metric spaces, compactness and connectedness, separation axioms.

Credit Hours : 3

Prerequisites

• MATH215 with a minimum grade D

Course Learning Outcomes

1. Apply topological methods to solve other problems.
2. Explain basic topological concepts to their peers.
3. Justify proofs of several standard theorems related to compactness, connectedness and separation axioms.
4. Summarize the fundamental concepts of topology, including: topological spaces, continuity, metric spaces, and hausdorff spaces.
5. Survey literature related to a given topological problem.

Mathematical Modeling (MATH470)

The modeling process, dimensional analysis, model fitting techniques, discrete models difference equations, logistic equation. Continuous models using derivatives for example: predator-prey, population, harvesting, models. Discussion of stability, phase plane. Applications using Mathematica.

Credit Hours : 3

Prerequisites

• MATH275 with a minimum grade D

Course Learning Outcomes

1. Analyze quantitatively the findings of a model.
2. Build team work for their mathematical modelling part of a project.
3. Develop a mathematical model for a given problem.
4. Prepare a report linking a given problem with its model.
5. Understand modeling process, dimensional analysis, model fitting techniques, discrete models difference equations and logistic equation.
6. Use technology to obtain relevant solutions of a model.

Control Theory & Applications (MATH471)

Introduction and motivation. Problem formulation. Systems models: linear and nonlinear systems. Optimal control problems arising from different fields. Calculus of variation with application to system modeling. Limitation of calculus of variation leading to modern control theory. Time optimal control, attainable state, reachable sets, and Bang-Bang principle. Pontryagin minimum principle and transversality conditions. Linear quadratic control problems. Optimal linear state feedback control. Applications: 3-axis attitude control of communication satellites, road building and fisheries problems, geo-synchronous satellites, speed controls of electric motors.

Credit Hours : 3

Prerequisites

• MATH275 with a minimum grade D
• MATH320 with a minimum grade D

Course Learning Outcomes

1. Classify mathematical representation of control systems.
2. Communicate effectively solutions of optimal control problems to their peers.
3. Determine the existence and uniqueness conditions of an optimal control using pontryagin maximum principle.
4. Show the stability, observability and controllability of a linear system.
5. Use logical approach of transfer functions to obtain state-space realizations from a linear system and vice versa.
6. Use numerical techniques and computer simulation to find solutions of optimality conditions.

Advanced Topics in Mathematics of Data Science (MATH475)

Special topics in Mathematics of Data Science is a unique course. The topics are selected from recent developments and trends in Mathmeatics of Data Science. The course may introduce new or emerging aspects in the field, contemporary applications and theory in Mathematics of Data Science, or assesses the state-of-the-art through readings, discussions, and critiquing current literature.

Credit Hours : 3

Prerequisites

• MATH290
• CSBP224

Course Learning Outcomes

1. Evaluate the feasibility and applications of the specialized topic.
2. Recognize the methods, techniques, and skills specific to the topics.
3. Apply the specialized methods, techniques, and skills in the topics.

Research Project (MATH495)

Students are supervised during their formulation of research proposals. Instructors direct their students in carrying out different tasks leading to the execution of the projects. Students are required to give presentations regarding their achievements, and the written final reports are submitted for evaluation.

Credit Hours : 3

Corequisites

• MATH500

Course Learning Outcomes

1. Find appropriate references and read mathematical literature independently.
2. Prepare reports in mathematics using appropriate tools (e.g. SWP and/or LaTeX) and computational software.
3. Present mathematical results orally in front of a specialized audience.
4. Explain mathematical results in a deductive way.
5. Work on assigned tasks related to mathematics individually and as a part of a group.
6. Demonstrate knowledge of mathematics beyond the curriculum.

Research Project I (MATH496)

Students are supervised during their formulation of research proposals. Instructors direct their students in carrying out different tasks leading to the execution of the projects. Students are required to give presentations regarding their achievements, and the written final reports are submitted for evaluation.

Credit Hours : 3

Prerequisites

• STAT470 with a minimum grade D
• CSBP224 with a minimum grade D

Course Learning Outcomes

1. Find appropriate references and read mathematical literature related with data science independently.
2. Prepare reports in mathematics of data science using appropriate tools (e.g. Python, Mathematica, MATLAB, SWP and/or LaTeX) and computational software.
3. Present mathematical results related to the data science orally in front of a specialized audience.
4. Explain mathematical results related to data science in a deductive way.
5. Work on assigned tasks related to mathematics and data science individually and as a part of a group.
6. 6. Demonstrate knowledge of mathematics and data science beyond the curriculum.

Research Project II (MATH497)

Students are supervised during their formulation of research proposals. Instructors direct their students in carrying out different tasks leading to the execution of the projects. Students are required to give presentations regarding their achievements, and the written final reports are submitted for evaluation.

Credit Hours : 3

Prerequisites

• MATH496 with a minimum grade D

Course Learning Outcomes

1. Find appropriate references and read mathematical literature related with data science independently.
2. Prepare reports in mathematics of data science using appropriate tools (e.g. Python, Mathematica, MATLAB, SWP and/or LaTeX) and computational software.
3. Present mathematical results related to the data science orally in front of a specialized audience.
4. Explain mathematical results related to data science in a deductive way.
5. Work on assigned tasks related to mathematics and data science individually and as a part of a group.
6. Demonstrate knowledge of mathematics and data science beyond the curriculum.

Internship (MATH500)

The Internship training program is coordinated by both the department, academic supervisor and the faculty training committee. The program is continuously monitored and reviewed by a field supervisor staff member at one of the institutions, establishments, or work sites in the United Arab Emirates. (This course is conducted over half a semester (8 weeks) during the third year of study. Offered condensed courses should be taken during the other half of the semester).

Credit Hours : 6

Prerequisites

• Pre/Co MATH495 with a minimum grade D

Course Learning Outcomes

1. Apply their mathematical knowledge in a working environment.
2. Determine an adequate mathematical model for a problem coming from the industry.
3. Develop adequate team work attitudes with supervisors, co-workers, and possibly customers.
4. Interpret a problem from the industry in a logical, scientific way.
5. Make up a well-structured and organized professional report.
6. Solve mathematical problems coming from the industry.

Internship (Mathematical Analytics) (MATH501)

The Internship training program is coordinated by both the department, academic supervisor and the faculty training committee. The program is continuously monitored and reviewed by a field supervisor staff member at one of the institutions, establishments, or work sites in the United Arab Emirates. (This course is conducted over a semester (16 weeks) during the final year of study.

Credit Hours : 6

Prerequisites

• MATH496 with a minimum grade D

Course Learning Outcomes

1. Apply their Mathematical Analytics knowledge in a working environment.
2. Solve mathematical problems related to data science coming from the industry.
3. Use an adequate mathematical model to solve Data Science related problems.
4. Interpret a problem from the industry in a logical, scientific way.
5. Make up a well-structured and organized professional report.
6. Develop adequate team work attitudes with supervisors, co-workers, and possibly customers

Real Analysis (MATH510)

Sequences of functions, the uniform norm, uniform convergence. Series of functions and tests for uniform convergence. Limits superior and inferior. Lebesgue outer measure and Lebesgue measure, measurable subsets, Borel measurable sets, non-measurable sets. Measurable functions. Integration of non-negative functions, Levi’s monotone convergence theorem, Fatou’s lemma, Integrals of measurable functions. Lebesgue’s dominated convergence theorem. Riemann integral versus Lebesgue integral.

Credit Hours : 3

Course Learning Outcomes

1. Compare Riemann Integrals With Lebesgue Integrals
2. Construct Lebesgue Outer Measure And Lebesgue Measure
3. Describe ?-Algebras, Measurable Sets, And Measurable Functions
4. Explain Lebesgue Integrals And Convergence Theorems
5. Justify Pointwise And Uniform Convergence Of Sequences And Series Of Functions

Complex Analysis (MATH515)

Complex derivative, Cauchy-Riemann equations, conformality, power series and Abel's theorem. Complex integration, exactness and independence of path, Cauchy's theorem for disks, Cauchy's integral formula, higher derivatives, applications. Taylor's finite development, zeroes of analytic functions, classification of isolated singularities, Casorati-Weierstrass theorem. Argument principle, open mapping theorem. Maximum modulus principle, Schwarz' lemma. Chains, cycles, simple connectivity, homology, general form of Cauchy's theorem, periods and residues, the residue method. Compactwise convergence and Weierstrass' theorem, Hurwitz's theorem, Taylor's expansion, Laurent expansion. Mittag-Leffler’s theorem. Infinite products and absolute convergence, Weierstrass’s factorization theorem. Riemann conformal mapping theorem. Montel's theorem. Special functions (gamma, zeta). Introduction to Harmonic analysis.

Credit Hours : 3

Course Learning Outcomes

1. Analyze Entire Functions
2. Apply Techniques Of Complex Analysis To Solve Boundary Value Problems.
3. Describe Techniques Of Power Series Expansions
4. Discuss Important Concepts And Results Of Complex Analysis

Numerical Analysis (MATH520)

Error analysis. Solutions of linear systems: LU factorization and Gaussian elimination, QR factorization, condition numbers and numerical stability, computational cost. Least squares problems: the singular value decomposition (SVD), QR algorithm, numerical stability. Eigenvalue problems: Jordan canonical form and conditioning, Schur factorization, the power method, QR algorithm for eigenvalues. Iterative Methods: construction of Krylov subspace, the conjugate gradient and GMRES methods for linear systems, the Arnoldi and Lanczos method for eigenvalue problems.

Credit Hours : 3

Course Learning Outcomes

1. Assess A Method For Its Accuracy, Stability, And Computational Cost.
2. Demonstrate Awareness Of The Efficiency Implications In A Computer Implementation Of A Method.
3. Model A Real-World Problem As A Problem In Numerical Linear Algebra.
4. Select Or Design A Method For Solving A Problem In Numerical Linear Algebra.
5. Use Numerical Software Appropriately, I.E., Decide When To Use Certain Methods Depending On Their Limitations.

Numerical Methods in Differential Equations (MATH522)

Theory and implementation of numerical methods for initial and boundary value problems in ordinary differential equations. One-step, linear multi-step, Runge- Kutta, and extrapolation methods; convergence, stability, error estimates, and practical implementation, Study and analysis of shooting, finite difference and projection methods for boundary value problems for ordinary differential equations. Theory and implementation of numerical methods for boundary value problems in partial differential equations (elliptic, parabolic, and hyperbolic).

Credit Hours : 3

Course Learning Outcomes

1. Analyze The Error Associated With Each.
2. Create Computer Algorithms For Solving Ordinary And Partial Differential Equations.
3. Implement Different Numerical Methods For Solving Initial And Boundary Value Problems.
4. Implement Different Numerical Methods For Solving Various Types Of Partial Differential Equations Such As The Elliptic, Parabolic And Hyperbolic Types.
5. Solve Real-Life Problems Using Different Numerical Methods.

Algebra I (MATH540)

Group theory: definitions, subgroups, permutation groups, cyclic groups, quotient groups, homomorphism, the isomorphism and the correspondence theorems. Ring theory: definitions, rings homomorphism, ideals, quotient rings, fraction fields, polynomial rings, Euclidean domain, and unique factorization domain. Field theory: algebraic field, extensions.

Credit Hours : 3

Course Learning Outcomes

1. Classify Different Algebraic Structures.
2. Define Various Algebraic Structures.
3. Determine Galois Groups Of Polynomials Of Small Degree, Of Finite Fields And Of Cyclotomic Number Fields.
4. Develop Proofs Of General Statements Using Abstract Knowledge And Operations.
5. Develop Proofs Of General Statements Using Abstract Knowledge.
6. Justify Steps Of Abstract Proofs By Invoking Properties Of Algebraic Structures.

Number Theory (MATH541)

Basics of number theory: divisibility, unique factorization, congruence arithmetic, Chinese remainder theorem, integers modulo n, Finite fields, Fermat's little theorem, and Wilson's theorem. Introduction to Algebraic number theory: the Pell equation, the Gaussian integers, Quadratic integers, and the Four square theorem. Quadratic reciprocity and quadratic congruence with composite modules.

Credit Hours : 3

Course Learning Outcomes

1. Apply Public-Key Cryptography
2. Identify And Use The Properties Of Congruences
3. Identify And Use The Properties Of Divisibility
4. Recognize The Theory Of Quadratic Residues And Their Relation To Quadratic Congruences
5. Solve Some Diophantine Equations

General Topology (MATH561)

Fundamentals of point set topology: topological spaces, neighborhoods of points, basis, subbases, and weight of spaces. Continuous maps and homeomorphisms, closed and open mappings, quotient mappings. Metric and normal spaces, accountability and separation axioms. Product spaces and quotient spaces. Compactness and connectedness of spaces and properties. Complete metric space and function spaces.

Credit Hours : 3

Course Learning Outcomes

1. Examine The Main Topological Properties Of Metric Spaces .
2. Justify Elementary Theorems Involving The Concepts Of Topological Spaces, Homeomorphism, Compactness And Connectedness.
3. Outline The Fundamental Concepts Of Point-Set Topology.
4. Solve Mathematical Questions By Using General Topology.

Theory of Partial Differential Equations (MATH570)

The theory of initial value and boundary value problems for hyperbolic, parabolic, and elliptic partial differential equations, with emphasis on nonlinear equations. More general types of equations and systems of equations

Credit Hours : 3

Course Learning Outcomes

1. Apply Analytical Methods To Prove Existence And Uniqueness Of Solution For Different Types Of Pde’S.
2. Classify Second Order Linear Pdes.
3. Construct Green’S Functions And Green’S Representation Formula.
4. Solve Various Types Of Pde’S Using Different Tools, Such As: The Method Of Characteristics, Separation Of Variables, Fourier And Laplace Transforms.
5. Use The Software Mathematica To Solve Various Types Of Pde’S.

Dynamical Systems & Chaos Theory (MATH573)

Discrete time dynamical systems. Continuous time dynamical systems. Invariant manifolds, homoclinic orbits, local and global bifurcations. Hamiltonian systems, completely integrable systems, KAM theory. Different mechanisms for chaotic dynamics, symbolic dynamics, Applications in physics, biology and economics.

Credit Hours : 3

Course Learning Outcomes

1. Use Sarkovskii’S Theorem And Period Doubling Cascades In Chaos Theory.
2. Apply Chaotic Dynamical Systems To Science And Engineering Problems.
3. Describe The Different Mechanisms Governing Chaotic Systems.
4. Determine The Stability And Bifurcations Of Non-Linear Discrete And Continuous Dynamical Systems.
5. Explain The Poincaré-Bendixson Theorem And Its Various Implications.
6. Explain  The Concepts Of Chaos And Fractal

Functional Analysis (MATH616)

Banach Spaces, The Banach Fixed Point Theorem; Bounded Linear Operators and functionals; Hilbert Spaces; Representation of functionals on Hilbert Spaces; Compact linear operators in Banach Spaces; Spectral Theory of Bounded Self-Adjoint Linear Operators in Hilbert Spaces.

Credit Hours : 3

Course Learning Outcomes

1. Recognize Inner Products, Hilbert Spaces, And State Their Main Properties
2. Recognize Normed And Banach Spaces, And State Their Main Properties
3. Solve Problems Related To Linear Operators On Hilbert And Banach Spaces.
4. Work With Metric Spaces

Mathematics Seminar (MATH633)

In the first part of the course, introductory research talks will be delivered by faculty members. Ethics issues related to mathematical research will be also discussed. In the second part, each student will give a talk in a research topic of his/her choice.

Credit Hours : 1

Course Learning Outcomes

1. Demonstrate ability to integrate in depth specific scientific topics;
2. Explain mathematical ideas in a rigorous and ethical way;
3. Develop effective team work with peers on projects related to the assigned topics;
4. Compile a literature review related to the assigned topics;

Algebra II (MATH640)

Group theory: Sylow theorems, Jordan–Holder theorem, solvable group. Ring theory: unique factorization in polynomial rings and principal ideal domain. Field theory: rules and compass constructions, roots of unity, finite fields, Galois theory, solvability of equations by radicals.

Credit Hours : 3

Course Learning Outcomes

1. Justify Steps Of Abstract Proofs By Invoking Properties Of Algebraic Structures.
2. Produce Properties Of A Group From The Properties Of Its Subgroups.
3. State The Properties Of Basic Algebraic Structures.

Selected Topics (MATH690)

A variety of topics and current research results in Mathematics will be presented by faculty members to students.

Credit Hours : 3

Course Learning Outcomes

1. Build Various Mathematical Tools Related To The Topics, And Apply Them To The Investigation Of Other Problems When Appropriate.
2. Construct Clear And Accurate Mathematical Proofs In Exploring Further Properties Of The Studied Mathematical Objects.
3. Explain The  Concepts And Tools Developed In The Topics
4. Solve Multi-Steps Problems Using Advanced Mathematics Tools.
5. Survey The Main Results Of A Specific Mathematic Topic.

Independent Studies (MATH695)

Graduate students will study topics related to their Ph.D. thesis independently. The selection of these topics will be with the consent of advisor.

Credit Hours : 3

Course Learning Outcomes

1. Explain The Concepts Of The Studied Topics.
2. Formulate Mathematical Proofs In A Clear, Rigorous And Accurate Scientific Method By Exploring The Properties Of The Studied Mathematical Objects.
3. Manipulate The Tools Of The Studied Topics.

Functional Analysis (MATH710)

Normed Spaces; Banach Spaces; Compactness and Finite Dimension; Bounded Linear Operators; Operator Spaces; Inner Product Spaces; Hilbert Spaces; Orthonormal Sets and Sequences; Representation of Functionals on Hilbert Spaces; Self-Adjoint; Unitary and Normal Operators; Zorn's Lemma; The Hahn-Banach Theorem; Adjoint Operator; Reflexive Spaces, The Baire Category Theorem; The Uniform Boundedness Theorem; Strong and Weak Convergence; Numerical Integration and Weak-* Convergence; The Open Mapping Theorem; The Closed Graph Theorem; The Banach Fixed Point Theorem; Spectral Theory of Bounded Linear Operators and Compact Linear Operators in Normed Spaces; Spectral Theory of Bounded Self-Adjoint Linear Operators in Hilbert Spaces.

Credit Hours : 3

Course Learning Outcomes

1. Elaborate with dual spaces and their properties
2. Recognize weak topologies on Banach spaces and their properties
3. Elaborate with linear bounded operators on Hilbert spaces and state their main properties.
4. Examine spectral properties of linear operators on Hilbert spaces.

Advanced Measure Theory (MATH715)

Outer Measure; The Caratheodory-Hahn Theorem; Measurable Functions; Integration of Measurable Functions; Fatou’s lemma and Convergence Theorems; Abstract Measure Spaces; Product Measures; Fubini’s Theorem; Abstract L_p Spaces; The Completeness of L_p (X,μ); The Riesz Representation Theorem for the Dual of L_p (X,μ).

Credit Hours : 3

Prerequisites

• MATH510

Course Learning Outcomes

1. Identify abstract measure spaces
2. Apply integration of measurable functions.
3. Explain the classical Banach spaces; L^p-spaces.
4. Explain product measures and Lebesgue measure on R^n.

Introduction to Operator Algebras (MATH716)

Banach and Hilbert spaces. Bounded operators on Hilbert spaces, Algebras of operators, The von Neumann algebras, compact and Hilbert-Schmidt operators. Abstract C*-algebras and main examples, The Continuous Functional Calculus Theorem, polar decomposition, Gelfand-Naimark Theorem.

Credit Hours : 3

Prerequisites

• MATH616

Course Learning Outcomes

1. Analyze properties of bounded linear operators on Hilbert spaces
2. Explain main properties of von Neumann algebras
3. Analyze theorems of abstract C*-algebras

Numerical Methods for Partial Differential Equations (MATH720)

Numerical quadrature, Spectral Methods: Collocation, Tau and Galerkin methods, Elliptic Problems and the Finite Element Method: conservation of heat, behavior of solutions, Two-point boundary value problems and the Laplace and Poisson equations, variational and the Galerkin finite element methods, Convergence, finite difference method, Method of Lines, Numerical stability, stiffness and dissipativity, convergence, Finite difference schemes, consistency, Stability, dissipativity, dispersion, convergence.

Credit Hours : 3

Prerequisites

• MATH522

Course Learning Outcomes

1. Approximate the solution of partial differential equations using the spectral methods.
2. Obtain the numerical solution of partial differential equations using the finite element methods.
3. Approximate the solution of class of partial differential equations using the finite difference methods.
4. Compute the region of stability of a numerical method for approximating the solution of partial differential equations.

Advanced Algebra (MATH740)

Modules, quotient modules, module homomorphisms, direct sums, free modules, tensor products. Vector spaces, matrices, dual spaces, determinants. Modules over principal ideal domains, rational canonical forms, Jordan canonical form. Modules over group rings, Schur lemma, Wedderburn theorem, character theory, orthogonality relations.

Credit Hours : 3

Prerequisites

• MATH640

Course Learning Outcomes

1. Explain basic concepts of module theory.
2. Analyze quotient modules, homomorphisms, and basic operations on modules.
3. Employ vector spaces and determinants.
4. Describe modules over principal ideal domains and determine canonical forms of matrices.
5. Analyze basic concepts of representation theory.
6. Apply character theory and orthogonal relations.

Advanced Number Theory (MATH741)

Estimates of arithmetic functions, the prime number theorem, Dirichlet series, Dirichlet theorem on primes in arithmetic progressions. Integer partitions, Euler formulas, Jacobi triple product formula. Algebraic numbers, algebraic integers, quadratic fields, units and primes in quadratic fields.

Credit Hours : 3

Course Learning Outcomes

1. Apply estimates of arithmetic functions to analytic number theory.
2. Describe the Prime Number Theorem.
3. Solve problems related to integer partitions.
4. Analyze Diophantine equations using Algebraic Number Theory.

Cryptography (MATH743)

Public key cryptosystems (RSA, Rabin, ElGamal), discrete logarithm, Diffie-Hellman key exchange, primality testing, factoring algorithms, multivariate cryptography, other systems, signature schemes, secret sharing, hash functions, identification.

Credit Hours : 3

Prerequisites

• MATH540 with a minimum grade D

Course Learning Outcomes

1. Employ basic concepts and algorithms of cryptography
2. Perform encryption and decryption using public key cryptograph
3. Analyze and apply digital signature schemes, secret sharing, hash functions, identification.
4. Examine the discrete logarithm problem and illustrate its applications.
5. Formulate the mathematical concepts underlying modern cryptography.

Coding Theory (MATH744)

Block codes, linear codes, generator and parity check matrices, dual codes, weight and distances, weight enumerators, Hamming codes, Golay codes, Reed-Muller codes, Kerdock codes, bounds on codes, theory of cyclic codes, BCH codes, Reed-Solomon codes, quadratic residue codes, generalized Reed-Muller codes, codes over Z4.

Credit Hours : 3

Course Learning Outcomes

1. Employ basic notions of coding theory.
2. Analyze linear codes and describe their generator and parity-check matrices.
3. Categorize different types of codes
4. Determine codes over rings
5. Formulate the mathematical concepts underlying coding theory.

Finite Fields and Applications (MATH745)

Fields, finite fields, field extensions, trace and norm functions, bases, polynomials, primitive polynomials, irreducible polynomials, linearized polynomials, applications of finite fields, linear codes, multivariate cryptography.

Credit Hours : 3

Prerequisites

• MATH740

Course Learning Outcomes

1. Describe the structure of finite fields.
2. Explain the notion of an extension of a field.
3. Produce computations in specific examples of finite fields.
4. Use polynomials for analyzing finite fields.

Finite Groups (MATH746)

Characteristic subgroups, Nilpotent and Solvable Groups, Semidirect and Central products; Automorphisms as Linear Transformation. Representations of Finite Abelian Groups, Complete Reducibility, Clifford’s Theorem, G-Homomorphism and Representation of direct and central products, Character Theory: Frobenius Groups, Coherence and Brauer’s characterization of Characters.

Credit Hours : 3

Prerequisites

• MATH540

Course Learning Outcomes

1. Explain the main concepts of and theorems of the theory of finite groups.
2. Apply structural results for finite groups.
3. Employ the main concepts of representation theory.
4. Demonstrate the efficient use of character theory.

Module and Ring Theory (MATH747)

Free module, Projective module, Injective module, Flat modules, Homological dimensions, Noncommutative localization, von Neumann regular rings and generalizations, Frobenius and quasi-Frobenius rings, Morita theory.

Credit Hours : 3

Prerequisites

• MATH640

Course Learning Outcomes

1. Recognize and characterize several types of modules (e.g. free modules, projective modules, injective modules)
2. Describe several types of rings (e.g. von Neumann regular rings, Frobenius and quasi-Frobenius rings)
3. Explain the relationship between some types of modules and particular rings
4. Establish the equivalences between two categories of modules

Topology (MATH760)

Topological spaces, basis, subbases, continuous maps and homeomorphisms, product and quotient topology, metric and normed spaces, connectedness, local connectedness, compactness, local compactness, compactification, countability axioms, normal spaces, Uryshon metrization Theorem, Tietze Extension Theorem, manifolds, complete metric space and function spaces, Metrization Theorem and paracompactness.

Credit Hours : 3

Prerequisites

• MATH561

Course Learning Outcomes

1. Analyze basic properties of topological spaces.
2. Apply various mappings such as continuous mappings, homeomorphisms, closed and open mappings, and quotient mappings.
3. Decide whether two given spaces are homeomorphic.
4. Construct topological spaces to prove or disprove a given statement.

Algebraic Topology (MATH761)

Fundamental group and covering spaces, simplicial and singular homology theory with applications, cohomology theory, duality theorem. Homotopy theory, fibration, relations between homotopy and homology, obstruction theory, and topic from spectral sequences, cohomology operations, and characteristic classes.

Credit Hours : 3

Prerequisites

• MATH760

Course Learning Outcomes

1. Demonstrate understanding of the fundamental concepts of algebraic topology, such as homotopy and homology.
2. Demonstrate familiarity with a range of examples illustrating these notions.
3. Compute the fundamental group and the homology groups of many examples of topological spaces.
4. Demonstrate proficiency in communicating mathematics orally and in writing.

Knot Theory and Applications (MATH763)

Knots and links, isotopy, Reidemeister moves, numerical invariants, 3-colorings, Braids, Alexander’s Theorem and Markov moves, Jones and bracket polynomials, Tait’s conjectures, Alexander-Conway polynomial, HOMFLY and Kauffmann invariants, Tangle equations and Applications.

Credit Hours : 3

Course Learning Outcomes

1. Explain the fundamental notions of knot theory.
2. Solve low dimensional topological problems using knot theory.
3. Compare between different type of knot invariants.
4. Apply knot theoretical techniques to biological problems

Differential Manifold (MATH764)

n-dimensional Euclidean Space, curves and surfaces, coordinate charts, manifolds, smooth maps, immersion and imbedding, sub-manifolds, partitions of unity, tangent vectors and cotangent vectors, tangent bundles, Riemannian manifolds, tensor and exterior algebra, differential forms, exterior differentiation, de Rham cohomology, Lie groups, quotient spaces.

Credit Hours : 3

Course Learning Outcomes

1. State the abstract definition of manifolds in the Euclidean space.
2. Apply tools from Advanced Calculus to the study of manifolds.
3. Understand the geometry and topology of curves and surfaces.
4. Apply methods of abstract algebra to the analysis of manifolds.
5. Understand the geometric terms defined on higher-dimensional manifolds.

Advances Partial Differential Equations II (MATH770)

Method of characteristics, Formation of shocks for the inviscid Burger’s equation, Explicit formulas for solutions of the linear wave equation (d’Alembert, Kirchoff, Poisson) , Cauchy-Kowalevski theorem •, Review of Gronwall’s inequality, Sobolev spaces, Picard iteration, definition of an initial value problem, local well-posedness, global well-posedness, Green’s theorem, Review of Fourier analysis, Vector fields, Energy estimates, Finite speed of propagation, Klainerman-Sobolev inequality (preceded by a review of Sobolev embeddings), Local well-posedness for quasi-linear wave equations and global well-posedness in a subcritical situation, Symmetric hyperbolic systems, Small data global well-posedness for quasi-linear wave equations on R^n , n ≥ 4 Small data global well-posedness for quasi-linear Klein-Gordon equation on R^3, Null forms and small data global well-posedness for quasi-linear wave equations on R^3.

Credit Hours : 3

Course Learning Outcomes

1. Analyze Method of characteristics.
2. Formation of shocks for the inviscid Burger’s equation
3. Find explicit formulas for solutions of the linear wave equation.
4. Proof the related theorems Sobolev spaces.
5. Anlayize the local well-posedness for quasi-linear wave equations and global well-posedness in a subcritical situation and symmetric hyperbolic systems.
6. Analyize the small data global well-posedness for quasi-linear wave equations on R^n , n ≥ 4 .

Integral Equations and Calculus of Variations (MATH771)

Integral Equations: Definition of Integral Equations, Kinds of Kernels, Volterra and Fredholm Equations, Method of Successive Approximations, Applications to O.D.E’s, Green’s Functions, Complex Form of Fourier and Laplace Transforms, Singular Integral Equations, Symmetric Kernels, Eigenvalues and Eigenfunctions, Fundamental Properties of Eigenvalues and Eigenvectors, Hilbert-Schmidt Theorem, Rayleigh-Ritz Method for Finding the First Eigenvalue. Calculus of Variations: Maxima and Minima, Euler Equation, Constraints and Lagrange Multipliers, Hamilton’s Principle, Lagrange Equations.

Credit Hours : 3

Course Learning Outcomes

1. Approximate the solution of integral equations of different types.
2. Explain the applications of calculus of variations.
3. Approximate the solution of eigenvalue problems.
4. Implement the different approaches in calculus of variation to solve real life problems.
5. Analyze the singular integral equations.

Theory of Ordinary Differential Equations (MATH772)

Initial Value Problem: Existence and Uniqueness of Solutions; Continuation of Solutions; Continuous and Differential Dependence of Solutions. Linear Systems: Linear Homogeneous And Nonhomogeneous Systems with Constant and Variable Coefficients; Structure of Solutions of Systems with Constant and Periodic Coefficients; Higher Order Linear Differential Equations; Sturmian Theory, Stability: Lyapunov Stability and Instability. Lyapunov Functions; Lyapunov's Second Method; Quasilinear Systems; Linearization; Stability of an Equilibrium and Stable Manifold Theorem for Nonautonomous Differential Equations.

Credit Hours : 3

Course Learning Outcomes

1. Describe The Concept Of Stability Of Solutions Of Differential Equations Of Higher Orders And Systems.
2. Explain The Concepts Of Equilibrium And Stable Manifolds And Their Role In Stability Of Solutions Of Ode’S.
3. Prove The Existence And Uniqueness Of Solutions Of Certain Class Of Ordinary Differential Equations.
4. Solve Systems Of Differential Equations Of Different Types.
5. Use Available Software To Solve Differential Equations And Systems Of Differential Equations.

Dynamical Systems and chaos theory (MATH773)

Discrete time dynamical systems. Continuous time dynamical systems. Invariant manifolds, homoclinic orbits, local and global bifurcations. Hamiltonian systems, completely integrable systems, KAM theory. Different mechanisms for chaotic dynamics, symbolic dynamics. Applications in physics, biology and economics.

Credit Hours : 3

Prerequisites

• MATH275 with a minimum grade D

Course Learning Outcomes

1. Locate and analyse the stability of fixed points and periodic orbits of maps and flows
2. Identify commonly encountered local and global bifurcations.
3. Understand the basic notions of chaotic behaviour for maps and flows.
4. Define integrability of Hamiltonian systems, and give a qualitative and semi-quantitative analysis of perturbed integrable dynamics.
5. Identify applications of each of the main classes of dynamical systems, stating features of their long time behaviour.

Stochastic Calculus for Finance (MATH774)

Stochastic process; Brownian motion; Martingales; Ito's integral; Ito's formula; Stochastic differential equations; Geometric Brownian motion; Arbitrage and SDEs; The diffusion equation; Representation theorems; Risk-neutral measures, Change of measure and Girsanov’s theorem; Arbitrage and martingales; The Feynman-Kac connection.

Credit Hours : 3

Course Learning Outcomes

1. Manipulate continuous stochastic processes such as Brownian motion.
2. Solve different kinds of stochastic differential equations.
3. Develop pricing formulas for some financial derivatives using Ito formula and representation and Girsanov theorems.
4. Construct PDEs for some financial derivatives prices using Feynman-Kac theorem.

Numerical Methods for Finance (MATH777)

Numerical differentiation (Forward, Backward, central), Measuring the error, Numerical instability, Finite difference methods, Monte-Carlo methods, the Euler-Maruyama and Milstein's higher order methods for Stochastic Differential Equations. Applications to finance such as the simulation of asset prices, Monte Carlo Evaluation of European options, numerical solution for the Black-Scholes PDE.

Credit Hours : 3

Course Learning Outcomes

1. Construct numerical simulations for SDEs.
2. Evaluate options using the Monte Carlo method.
3. Solve options pricing PDEs using the finite difference methods.
4. Develop solutions for derivative price using transform techniques.
5. Formulate calibration problems to estimate financial asset model parameters.

Independent Studies (MATH795)

Graduate students will study topics related to their MSc. thesis independently. The selection of these topics will be with the consent of advisor.

Credit Hours : 3

Course Learning Outcomes

1. Acquire an understanding of the studied topics.
2. Demonstrate familiarity with the concepts and tools of the studied topics
3. Be proficient in expressing clear and accurate mathematical proofs in exploring the properties of the studied Mathematical objects.

Algebra (MATU110)

The ALGEBRA COURSE is a blended learning course, which is designed to develop the algebra proficiencies necessary for success in future Math or Statistics courses. This course is required by all University colleges. It is covering the following topics: Linear equations and inequalities (in one variable),Operations on polynomials, Coordinate plane and Linear equations in two variables,Quadratic Equations and Rational Expressions,Setting up Equations and Problem Solving.

Credit Hours : 0

College Algebra (MATU120)

The COLLEGE ALGEBRA CURSE is a blended learning course, which is designed to develop students’ proficiencies in college algebra that are necessary for success in future Statistics or Calculus courses. This course is required by the Colleges of Business, IT, Engineering, Food and Agriculture, Medicine, Science, and Education (Science Tracks). It is covering the following topics: • Complex numbers • Factorial • Functions: properties, graphs, transformations, and inverse functions • Quadratic Functions: definition, zeros, vertices, and graphs • Exponential and logarithmic functions and equations • Basics of arithmetic and geometric sequences and sums

Credit Hours : 0

Trigonometry (MATU130)

The TRIGONOMETRY COURSE is a blended learning course, which is designed to develop the trigonometry proficiencies necessary for success in future Calculus courses. This course is required by the Colleges of IT, Engineering, Food and Agriculture, Medicine, Science, and Education (Science Tracks). It is covering the following topics: • Angles and their measures • Trigonometry of right triangles • Trigonometric functions: properties and graphs • Trigonometric identities and equations • Applications

Credit Hours : 0