This course is designed as an introductory accounting course in which the aim is to initiate the students in the use and preparation of financial statements.As aspiring managers,the students need to recognize the need for accounting principles,procedures and the financial statements in companies' decision making process.In so doing, the topics covered include the basic principles and recording process to prepare useful financial statements.In the second half of the semester,selected topics will be discussed in detail.

Topics related to both database design and database programming are covered.

The following topics will be included: regular expressions and contextfree languages, finite and pushdown automata, Turing machines, computability, undecidability, and complexity of problems.

Wellknown computational geometry problems, their algorithmic solutions and computational geometry problem solving techniques.

Greedy algorithms, divideandconquer type of algorithms, dynamic programming and approximation algorithms.

The course covers basics of Algorithms Analysis, graph theoretic concepts, greedy algorithms, divide and conquer algorithms, dynamic programming, and approximation algorithms.

The following topics will be included in the course: The main neural network architectures and learning algorithms, perceptrons and the LMS algorithm, back propagation learning, radial basis function networks, support vector machines, Kohonen’s self organizing feature maps, Hopfield networks, artificial neural networks for signal processing, pattern recognition and control.

The following topics will be included: Digital images as twodimensional signals; twodimensional convolution, Fourier transform, and discrete cosine transform; Image processing basics; Image enhancement; Image restoration; Wavelets and Multiresolution processing; Image coding and compression.

This course will extensively use algebra and basic calculus. The course focuses mainly on the following; static analysis, linear models and matrix algebra, comparative static models, optimization problems with equality constraints.

The following topics will be covered: First order differential and difference equations, higher order differential and difference equations, simultaneous systems of higher order equations, stability analysis.

Econometrics can be defined as the “application of statistics to the analysis of economic phenomena”.  The knowledge of econometrics is essential to test economic theories and to understand empirical work being done in Economics. The course will teach how to do empirical work by using examples drawn from various fields in economics. It will also focus on various types of economic data, how one can obtain them, and how they may be used. Topics include regression analysis, ordinary least squares, hypothesis testing, choosing independent variables and functional form, multicollinearity, serial correlation and heteroskedasticity.To aid in empirical work the regression package EViews will be used.

In this course money and monetary issues are explained. Foreign exchange is duly introduced with all experimental equations and practices. The scope and structures of all banking activities are taught and exemplified, and commercial banks’ role in the foreign trade are accentuated.

This course will explore the theoretical and empirical analysis of the effect of money on economy. The effect of money, credit and liquidity on income, employment, economic growth and inflation will be analyzed. The goals of monetary policy, the methods used to obtain these goals, and the effects of these methods will be discussed. Moreover, issues such as the functioning of monetary policy in international financial system; the relationship of the financial system with the real economy, monetary policy channels (money, bank credit, and balance sheet channels), and reasons and outcomes of inflation will be undertaken.

The course will teach advanced techniques that are required for empirical work in economics. Emphasis will be on the use and interpretation of single equation and system estimation techniques rather than on their derivation. The purpose of the course is to help students understand how to interpret economic data and conduct empirical tests of economic theories. It will focus on issues that arise in using such data, and the methodology for solving these problems. Specific topics include limited dependent variables, simultaneous equations, time series models, nonstationarity and cointegration and panel data analysis. The regression package EVIEWS will be used for empirical work. 

The course starts with an introduction to quantitative macroeconomics. We then discuss the benchmark deterministic model and competitive equilibrium. We then discuss steady state. The course continues with introduction to Matlab and Dynare programs in the Lab. It concludes with the discussion of calibration and simulation of a simple real business cycle (RBC) model.

The class covers the theory behind a variety of topics in time series econometrics, and introduces the student to a large number of time series applications. Class exposition is evenly divided between theory and applications, but applications are given priority in assignements and exams. After a brief review of statistical and econometric basics, we discuss the use of difference equations and lag operators. Stationary ARMA models are covered in great detail, and so are ARCH, GARCH, and VAR techniques. The student is also exposed to nonstationary time series, unit roots, and ARIMA models. The class ends with discussions on cointegration and forecasting.

The course covers the analysis of strategic behaviors in everyday life. Most of the times, people and firms are in competition and have to behave strategically to maintain their best interests. Behaving strategically means that an agent must accept other’s existence and consider their decisions as well when deciding. Our best interest may harm others whom we are living with. The merit is to find a (the) best solution maximizing the utility under given conditions.

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The course covers a broad range of topics in combinatorial modeling and the systematic analysis. The topics include basic counting rules, generating functions, recurrence relations, some famous combinatorial optimization problems and related mathematical techniques.

The field of quality control underwent major changes over the last two decades of the 20th century. These changes made quality control an important course in the curriculum of all major industrial engineering departments in the world. This course is designed to explain this evolutionary change in the understanding of quality concepts from different perspectives with the help of statistical methods.

Topics of this course include developing mathematical models and heuristic solution algorithms for essential Industrial Systems Engineering problems. During the course, IBM ILOG OPL Development Studio will be used to code and solve mathematical models and heuristic algorithms.

In this course, students will have the chance to learn certain optimization subjects, methods and models which are not covered in compulsory courses. At the end students will also have the chance to learn applications of these models and methods.

This course deals with the following topics: Models of manufacturing systems, including transfer lines and flexible manufacturing systems; Calculation of performance measures, including throughput, inprocess inventory, and meeting production commitments; Realtime control of scheduling; Effects of machine failure, setups, and other disruptions on system performance.

This course introduces the concept of heuristics to students who already know about mathematical optimization. The topics include basic heuristic constructs (greedy, improvement, construction); meta heuristics such as simulated annealing, tabu search, genetic algorithms, ant algorithms and their hybrids. The basic material on the heuristic will be covered in regular lectures The students will be required to present a variety of application papers on different subjects related to the course. In addition, as a project assignment the students will design a heuristic, write a code of an appropriate algorithm for the problem and evaluate its performance.

This course is one of the basic sections of Operations Research, which studies a rational process for selecting the best of several alternatives. The “goodness” of a selected alternative depends on the quality of the data used in describing the decision situation. From this standpoint, a decisionmaking process can fall into one of three categories.

1. Decisionmaking under uncertainty in which the data cannot be assigned relative weights that represent their degree of relevance in the decision process.
2. Decisionmaking under risk in which the data can be described by probability distributions.
3. Decisionmaking under certainty in which the data are known deterministically.
4. Decision making in multicriteria environment.

The main subjects of the course are the decision situation, decision rule, decision trees, information and the cost of additional information, utility theory, multiobjective problems, solution notions for such problems and methods for calculations efficient solutions for multiobjective problems, goal programming and the methods of analyzing solutions for goal programming problems.

Students will learn to make decisions by taking into account such features as interest rates, and rates of return. They will learn about the concept of arbitrage, and when consideration of such is sufficient to price different investments. Applications to call and put options will be given. Students will learn when arbitrage arguments are not sufficient to evaluate investment opportunities. They will learn to make use of utility theory and mathematical optimization models to determine optimal decisions. Dynamic programming will be introduced and used to solve sequential optimization problems. The use of simulation in financial engineering will be explored.

The main objective is to explore the primary theoretical and practical concepts that dominate international financial markets and those that should be taken into consideration during international risk management and investment decisions.

This lecture guides the students through a wide array of mathematics, ranging from elementary basic mathematics, limit, derivative and integral, linear algebra and differential calculus to optimization and linear regression. These quantitative methods are illustrated with a rich and captivating assortment of applications to the analysis of portfolios, derivatives, exchange, fixed income securities and equities.

Topics covered include specification of derivative instruments, pricing of futures and forwards, hedging with derivatives, trading strategies involving optiıons, BlackScholesMerton model

The content of this course will be comprised of mainly examining the structure of financial institutions and markets in developed countries and emerging markets as well as their interactions. The basic topics to be covered in this course are; the evolution of financial institutions, their role within the financial system, the operation of financial markets, their impact on economy, their future role and possible development strategies for financial markets.

This course studies the analysis of Martingales and Stationary Processes. The course analyzes the Poisson process in detail and extends it to renewal processes. The Brownian Motion and stochastic integration are also studied in the framework of this course.

This course targets to investigate physical events represented by partial differential equations in detail. This will include the derivation of the equations and the analysis of its mathematical structures, analytical and to some extent numerical solution techniques and interpretation the results in both mathematical and physical senses.

Topics include Boolean algebras, logic, set theory, relations and functions, graph theory, counting, combinatorics, and basic probability theory.

In this course basic concepts of basic set theory will be discussed. The Riemann integral; Measure, Null sets, Outer measure; Lebesque measurable sets and Lebesque measure; Monotone Convergence Theorems; Integrable functions, The Dominated Convergence Theorem.

In this course, division algorithm, Diaphontine equations, prime numbers and their distributions, the theory of congruences, number-theoretic functions, Fermat’ s theorem and its generalization, primitive roots and indices are discussed.

Students will learn to make decisions by taking into account such features as interest rates, and rates of return. They will learn about the concept of arbitrage, and when consideration of such is sufficient to price different investments. Applications to call and put options will be given. Students will learn when arbitrage arguments are not sufficient to evaluate investment opportunities. They will learn to make use of utility theory and mathematical optimization models to determine optimal decisions. Dynamic programming will be introduced and used to solve sequential optimization problems. The use of simulation in financial engineering will be explored.

Biological applications of difference and differantial equations. Biological applications of nonlinear differantial equations. Biological applications of graph theaory.

In this course numerical integration and their errors, numerical solution of ordinary differential equations (Picard, Euler, RungeKutta methods), numerical solution of boundaryvalue problems for ordinary differential equations, integrointerpolation method, numerical method for system of linearalgebraic equations with tridiagonal matrix, variational problems, Ritz and Galerkin methods will be discussed.

This course aims to cover basic theory and applications of Spectral Analysis.

This course aims to cover basic theory and applications of Spectral Analysis.

In this course basic concepts of Green's functions will be discussed. Application to the solution of ordinary differential equations; Fredholm and Volterra equations of the 1st and 2nd kinds; Fredholm equations with separable kernels.

In this course basic concepts of quaternions will be discussed. Clifford valued functions and forms; Clifford operator calculus; Boundary value problems.

In this course, the concepts of different computational methods are discussed. Student solve equations numerically and constract plots. As the application to probability theory and statistics, different simulation tethniques are studied.

In this course, the concepts of different computational and simulation methods are debated. The students analyse and test simulation techniques. Write programs. The students apply simulation to Probability Theory, Time Serious Analysis and Financial Mathematics.

The course covers basic concepts and applications of Fuzzy Set Theory.

This course focuses on the fundamentals of modern and classical numerical techniques for linear and nonlinear partial differential equations, with application to a wide variety of problems in science, engineering and other fields. The course covers the basic theory of scheme consistency, convergence and stability and various numerical methods.

Elements of a Game and Payoffs Games, Risk Sharing, insurance and option value and Intro to ComlabGames Software, Strategies, Sequential Move Games, Simultaneous Move Games, Nash Theory, Incomplete Information Games

Graphs notations, Varieties of graphs, Walks and Distance, Paths, Cycles, and Trees, colourability, chromatic numbers, five color theorem, four color conjecture,

The focus of the course will be the applications of abstract structures such as group actions, Sylow theorems, Gröbner bases, Galois theory, homology computations. This course is a complement of abstract algebra and enables students to understand abstract notions as solid structures.

Cryptography is one of the popular topics with direct applications to daily life. Topics include: congruences, factoring, quadratic residues as preliminaries from number theory and continue with cryptography; simple cryptosystems, publickey cryptosystems, practical applications such as authentication, key exchange and sharing.

The main subjects of the course are monomial orders, Groebner basis, elimination theory, dimension theory, resultants, Zariski topology and geometry-algebra bridge, affine and projective varieties, and invariant theory.

In this course, algebraic numbers are defined and their properties are investigated with motivations and roots from classical problems. Also, it makes abstract topics from algebra easier to understand. Nevertheless, the approach will be elementary and all necessary topics will be covered at the very beginning. Geometric methods will also be discussed with an applications of Minkowski's theorem.

This course provides an introduction to the theory of polynomial invariants with topics; linear representations, algebras, ring of invariants, permutation invariants, generators, bounds on generators, constructing invariants, system of parameters, and if possible, rational invariants. This course is a perfect chance to learn why abstract mathematics is a fundamental topic. It is intended for students planning professional career in various areas.

This course covers some fundamental topics about algebraic varieties. Projective geometry is also introduced and as a final topic homogeneous invariants of finite groups are studied. Algebraic geometry is a central topic which has tight connections with number theory, singularity theory, Diophantine problems. Prerequisites for this course are abstract algebra and multivariate calculus.

This course provides an introduction to error correcting codes by which it is possible to communicate on noisy channels, such as satellite communications. In this course, an introduction revealing the theory and also an introduction providing important classes of codes is aimed. Topics include: linear codes, Hamming codes as perfect codes, nonlinear codes, Hadamard codes, dual codes and weight distributions, cyclic codes, and BCH codes. Requirements for the course are basic linear algebra and an elementary number theory.

Through lectures and labs we aim to introduces the following classical mechanics and thermodynamics topic: space and time; straight line kinematics; motion in a plane; forces and static equilibrium; particle dynamics with force and conservation of momentum; relative inertial frames and noninertial force; work, potential energy and conservation of energy; rigid bodies and rotational dynamics; vibrational motion; conservation of angular momentum; central force motions; kinetic theory and the ideal gas; van der Waals equation of state, blackbody radiation, heat flow and the first law of thermodynamics; MaxwellBoltzmann distribution, random walk and diffusion; Carnot engine, entropy, and the second law of thermodynamics.

Topics covered are: identification, classification, measurement and management of different types of financial risks.

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Data and portfolio risk analysis will be learnt by using several approaches. Learning techniques requires an extensive use of Excelbased applications. JP Morgan’s RiskMetricsTM will also be covered during the course as a benchmark source for risk analysis and modeling.

In this course, students learn about the advanced topics in the process of video game development and use this information to develop their own computer games.

This course provides an introduction to Artificial Intelligence (AI). In this course we will study a number of theories, mathematical formalisms, and algorithms, that capture some of the core elements of computational intelligence. We will cover some of the following topics: search, logical representations and reasoning, automated planning, representing and reasoning with uncertainty, decision making under uncertainty, and learning.