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.

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 financial statements in the decision-making process of companies. In doing so, the topics covered include the basic accounting principles, recording process, and tools to prepare and analysis of the financial statements.

Data can be about anything. This course is about the data itself. Through this applied course students develop a critical perspective to identify data sources relevant to a problem in hand, learn how to: describe technologies and data management processes in contemporary corporate systems; combine and convert data across various sources, formats and standard; assess and improve data quality; articulate insights into a business or social science problem by visualizing and interpreting features of data and basic data analysis. The course consists of three modules: 1. Data and Life (4 weeks): Identifying sources of data in business and social sciences and what it represents. Translating theories and hypothesis to data. Sources and costs related to data. Data liabilities, ethics, security and theft, privacy concerns. Associational, relational, and geographic data; 2. Telling stories with data (5 weeks): Communicating analytics, using simple (Excel, Kaggle) plots in reports, infographics; 3. Managing data in the real world (5 weeks):SQL, RDBMS, data cleaning issues, unstructured data, the need for NoSQL databases in cloud and big data. Corporate ICT systems: storage and flow of data and information on-site and in cloud.

This course aims to develop data processing and analysis skills required in the fields of business and economics. In this course students learn computer coding skills focused on data processes, with case studies in their fields. In contrast to coding courses for students aiming an expertise in computing, this course approaches algorithms in terms of their function in business and economics problems and focuses on features and applications of data processing patterns. In this applied course students learn the programming languages Python and R, which are very common in business practice and research. In addition, the course covers the properties of big data analytics and technologies used for it. The course consists of three modules: 1-Big data (2 weeks): technologies (Hadoop, MapReduce), competencies, real time data processing, possible value creation pipelines in big data 2-Statistical processing with R (6 weeks): Exploratory statistics in R. 3-Introduction to coding for data analytics with Python (6 weeks): data types, searching/sorting, list processing for statistical calculations, web scraping for data

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.

Well-known 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: getting and cleaning data, exploring data, statistical models of data, statistical inference, main machine learning methods in data science including linear regression, SVM, k-nearest neighbors, Naïve Bayes, logistic regression, decision trees, random forests, clustering, and dimensionality reduction, over-fitting, cross-validation, feature engineering.

The course content includes: Digital images as two-dimensional signals; two-dimensional convolution, Fourier transform, and discrete cosine transform; Image processing basics; Image enhancement; Image restoration; Image coding and compression.

This course provides an introduction to basic models and concepts in microeconomics and macroeconomics. Basic topics in microeconomics analyzed in this course include an introduction to market economies, supply and demand, consumer theory, the theory of the firm, perfect competition. Basic topics in macroeconomics analyzed in this course include national income, employment / unemployment, inflation, money, banking and credit system.

Economics is the study of how people interact with each other, and with the natural environment, in producing their livelihoods. This course is an introduction to the basic principles of microeconomics, which analyzes the choices and actions of the economic actors as both self-interested and ethical. This course covers capitalist revolution; the effects of technological change; scarcity and opportunity cost; social interactions; the effect of institutions on balance of power; interactions among firm’s owners, managers and employees; profit maximizing firm’s interaction with its customers; supply, demand, and market equilibrium; market disequilibrium in credit and labor markets; market failures.

This course provides an introduction to the principles of macroeconomics, focusing on the fundamental concepts and tools used to analyze the economy as a whole. Students will explore key macroeconomic variables such as GDP, unemployment, inflation, and economic growth, and learn how to measure and interpret these indicators. The course covers the aggregate demand/aggregate supply (AD/AS) model to understand economic fluctuations and policy responses. Additionally, students will examine the roles of money, banking, and central banks in the economy, as well as the impact of monetary and fiscal policy on macroeconomic outcomes. By the end of the course, students will be able to analyze real-world economic issues, evaluate policy decisions, and apply economic reasoning to understand global and domestic economic trends.

This course covers basic calculus and focus on topics such as equilibrium analysis, comparative statics, and optimization.

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 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 economics. 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. 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.

The subjects of the course are the social and economic networks that permeate our social lives. Materials to be covered in the course include firm ownership networks and job search networks as well as friendship networks. Networks measures such as centrality, density and average distance will be discussed. Random link formation and strategic link formation will be examined. The spread of information, epidemics or opinions on the networks will be studied. In the course students will use network analysis software packages such as PAJEK, Dephi and igraph.

This course covers; quality costs and analysis, statistical methods (confidence intervals, hypothesis testing, analysis of variance), problem identification tools, control charts for variables and attributes, process capability analysis, measurement system analysis, statistical experimental design.

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 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.

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.

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 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.

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.

Interest Theory, Life Table, Life Annuity, Life Insurance, Premiums, Rezerves, Multiple Life Theory and Multiple Decrement Model.

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.

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.

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.

The main objective is to explore the modern financial management areas of corporate finance in an international setting with emphasis on those features that are unique to multinational corporation. This will require a general understanding of the international economic and monetary environment, plus a solid understanding of the basic tools of financial analysis.

This course covers, the evolution of risk management, enterprise risk management approach, fundamental concepts of risk management, goals and strategies in risk management, design and application of risk management systems.

This course covers the vital aspects of international trade ecosystem from the standpoint of both exporting and importing organizations. Within this context, strategy formulation, procedures and techniques, selection of import/export partners, and management of import/export transactions are covered.

The course contains the new financial ecosystem and the future of financial services. The course is supported by real-world cases and best-practices illustrating the themes of technology, business, and finance. These cases take an in-depth look at relevant topics to help describe and analyze the full breadth of the FinTech field.

The course focuses on the development of mathematical thought and the history of mathematics from the end of Antiquity to the present.

The course content is designed in twofold. In the first part the theoretical background of statistical analysis will be discussed. In the second part of the semester, demographic distribution, public health and the analysis and grouping of thee data in the healthcare sector will be discussed with examples.

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.

Smoothing of partial differential equations, modeling of displacement equation, wire oscillation and diffusion equations, Fourier transforms and applications, Fourier integral representations, Fourier transform method to wave and heat equation, Fourier cosine and sine transforms, solutions of problems in the semi-infinite range.

Topics include cartesian products, relations and functions, the pigeonhole principle, partition of integers, the exponential generating function, first and second-order linear recurrence relations, nonhomogeneous recurrence relations nonlinear recurrence relations.

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, divisibility algorithm, Diaphontine equations, prime numbers and distributions, conjugate theory, number-theoretical functions, Fermat's theorem and its generalization, prime roots, indices will be discussed.

This course provides a comprehensive introduction to financial mathematics, emphasizing practical applications of quantitative finance. Students will explore topics ranging from time value of money and portfolio optimization to advanced neural network models and big data analytics.

Chi-square distribution and applications, goodness of fit test, simple linear regression and correlation analysis, multiple regression analysis, non-linear regression analysis, defining model in multiple regression, single and multi-factor analysis of variance.

Applications of ordinary differential equations in biology will be introduced and their solutions will be examined using numerical methods.

This course aims to cover basic theory and applications of topology.

In this course the solution of linear systems of equations will be discussed using direct and iterative methods. Numerical integration and differentiation techniques and finding eigenvalues numerically 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 issues of Green's functions will be discussed. Solution applications of ordinary differential equations, Fredholm and Volterra equations of 1st and 2nd type, separable kernel and Fredholm equations will be studied.

In this course, basic issues of quaternions will be discussed. Clifford valued functions and forms; Clifford operator algebra; Boundary value problems will be studied.

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, various calculation methods are discussed. Students solve the equations numerically and draw graphs. Different simulation techniques are studied as probability theory and application of statistics.

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.

This course aims to teach basic theory and applications of Functional Analysis.

Elements of a Game and Payoffs Games, Prisoner's dilemma,Intro to ComlabGames Software, Strategies, Sequential Move Games, Risk and Probabilities, Simultaneous Move Games, Nash Theory, Incomplete Information Games

Graphs notations, Varieties of graphs, Walks and distance, paths, cycles, and trees, colorability, 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.

This course provides several basic methods for analyzing statistical data appear in various fields of science.

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 invariance theory. Topics include: linear representations, algebra, invariance rings, permutation invariants, generators, boundaries on generators, construction of invariants, system parameters and rational invariants.

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.

In this course, we will discuss the subjects of motion along a straight line, motion in two and three dimensions, Newton’s laws, work and kinetic energy, potential energy and conservation of energy, momentum, collisions, dynamics of rotations, gravitation and periodic motion.

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

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.

This course introduces the students to the fundamental concepts of programming using Python programming language.

This course introduces the students to the fundamental concepts of programming using Java programming language.

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.

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

In this course, students learn fundamental game design and development principles and methods for mobile platforms. Also students will gain practical knowledge by developing a mobile game as a course project.

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.