Course Contents

Must Courses (Thesis)

Smart factories (Industry 4.0) is the current trend of automation and data exchange in manufacturing technologies. It includes cyber-physical systems, the Internet of things and cloud computing. In smart factories machines, systems and networks are capable of independently exchanging and responding to information to manage industrial Production processes. In this course following topics will be covered: Cyber-physical structures, embedded systems, decentralized decision systems, flexible manufacturing systems, automation, man machine systems, human roles in intelligent systems (supervision).

 

The Internet of things (IoT) is the inter-networking of physical devices and other items to collect and exchange data. This course starts with an introduction to Internet of Things (IoT) products and services, including devices for sensing, actuation, processing, and communication. We discuss different cases of interactions, IoT protocols, and development platforms. The course includes a hands-on IoT project in order to experience concepts such as sensing, actuation and communication.

 

The objective of this course is to give the student the ability to apply artificial intelligence techniques, including search heuristics, knowledge representation, planning, reasoning and learning to various problems. In this course, the topics of uninformed search strategies; informed (heuristic) search strategies; adversarial search; propositional logic; predicate logic; supervised learning techniques; unsupervised learning techniques; natural language processing will be studied.

 

This course aims to provide graduate students with endowments to academically communicate their researches in an written and oral language. It provides them with comprehensive overview of various research skillsthat balance the practicalities of conducting research and the theory and debates that keep qualitative inquiry vibrant.It covers the entire research process including: formulating research questions; sampling (probability and nonprobability); measurement (surveys, scaling, qualitative, unobtrusive); research design (experimental and quasi-experimental); data analysis; and, writing the research paper. It also addresses the major theoretical and philosophical underpinnings of research including: the idea of validity in research, reliability of measures, scientific research ethics, and publication ethics.

 

Seminars are given by instructors, invited speakers and students enrolled in the course. Student presentations may also include thesis studies. In the course students will be informed about the things to consider in giving a successful presentation.

 

This is a thesis study of the student conducted by an academic advisor in the subject of Industry 4.0.

 

This is a thesis study of the student conducted by an academic advisor in the subject of Industry 4.0.

Smart factories (Industry 4.0) is the current trend of automation and data exchange in manufacturing technologies. It includes cyber-physical systems, the Internet of things and cloud computing. In smart factories machines, systems and networks are capable of independently exchanging and responding to information to manage industrial Production processes. In this course following topics will be covered: Cyber-physical structures, embedded systems, decentralized decision systems, flexible manufacturing systems, automation, man machine systems, human roles in intelligent systems (supervision)

 

The Internet of things (IoT) is the inter-networking of physical devices and other items to collect and exchange data. This course starts with an introduction to Internet of Things (IoT) products and services, including devices for sensing, actuation, processing, and communication. We discuss different cases of interactions, IoT protocols, and development platforms. The course includes a hands-on IoT project in order to experience concepts such as sensing, actuation and communication.

 

The objective of this course is to give the student the ability to apply artificial intelligence techniques, including search heuristics, knowledge representation, planning, reasoning and learning to various problems. In this course, the topics of uninformed search strategies; informed (heuristic) search strategies; adversarial search; propositional logic; predicate logic; supervised learning techniques; unsupervised learning techniques; natural language processing will be studied.

 

This course is designed to improve the analytical skills of students for business decision making and will provide insights into the basics of using the "Big Data" to quantify the operational impact of management decisions. This course includes modeling techniques, advanced data management, data visualization, optimization, risk analysis and simulation modeling.

 

EEE5101 Research Methods and Ethics (1+0)1 Credit ECTS:2

 

This course is a project study of the student conducted by an academic advisor on the subject of Industry 4.0.

This course is designed to improve the analytical skills of students for business decision making and will provide insights into the basics of using the "Big Data" to quantify the operational impact of management decisions. This course includes modeling techniques, advanced data management, data visualization, optimization, risk analysis and simulation modeling.

 

This course provides the introduction of the basic tools in Large Data Analysis and its implementation as an application. The main topics include Hadoop ecosystem, architecture, Apache Pig and Pig Latin language, data analysis using Hive architecture and Hive, Spark architecture, using Spark for data analysis.

 

The course aims to introduce advanced concepts in robot manipulation and control. The course objectives include:Outlining basic kinematic characteristics of robot manipulators, Providing a thorough analysis of robot dynamics, Contrasting the joint space and operational space formulations for robot dynamics, Introducing fundamental motion control strategies in the joint and operational spaces, Describing constraints, Outlining fundamental force control strategies, Introducing advanced path planning with potential fields, Explaining haptic rendering and haptic control of robot manipulators.

 

The aim of this course is to introduce the students to advanced theoretical treatment of robot dynamics and control and their applications. The course objectives include: Providing the students with a systematic method for deriving the equations of motion for a manipulator, Examining various trajectory generation methods, Explaining the independent joint control strategy, Introducing the computed torque method, Analyzing advanced robot control schemes including force control non-linear control and vision based control, Discussing advancements and emerging technologies in robotics control.

 

This course provides an introduction to data mining concepts. Basic concepts in data mining: frequent item set detection, association rules, clustering and classification are covered in depth. The students will learn how to approach data mining as a process, by demonstrating competency in the use of CRISP-DM, the Cross-Industry Standard Process or Data Mining, including the business understanding phase, the data understanding phase, the exploratory data analysis phase, the modeling phase, the evaluation phase, and the deployment phase. Also, they will understand and apply the most current data mining techniques and applications, such as text mining, mining genomics data, and other current issues.

 

This course is a hands-on course that requires writing software as well as board-level work. It sits at the intersection of fields such as microprocessors, digital design, operating systems, software design, and industrial automation. The students are exposed to topics such as meeting real-time constraints in embedded systems, generating delays and interrupts, using the serial interface, etc. They get theoretical as well as hands-on experience on embedded system design by using embedded software development environments and hardware emulators, as well as by working on actual hardware where they physically connect multiple building blocks.

 

This course is designed to lead the student to understand the importance and the nature of technological innovations, how they are integrated into business level strategies and how technological innovation process is managed. In this course, the aim is not only to understand the theories of technological innovations but also to discuss the practice of technological innovation. Therefore case studies are important; most of the theoretical parts are followed by case studies.

 

Aim of the course is to show the students how to create a competitive advantage through OM in the marketplace by conveying a set of skills and tools they can actually apply.

The students who have succeeded in this course; Discussing and developing a production strategy, Forecasting the demand and identify the elements of the demand, Identify the winning product/service characteristics, Define the quality for a product/service, Identify the different capacity management strategies, Defining and managing the inventory, Differentiate the different production management approaches such as lean vs. MRP, Scheduling operations.

 

To compete in the continuously advancing world, companies need to innovate and create novel and useful ideas. One of the most difficult tasks in any organization is to establish and manage a creative and innovative culture. This course provides an introduction to innovation and creativity management and how they lead to entrepreneurship. The quantitative and qualitative methods for choosing and managing the projects will be examined.

 

This course is designed for junior level industrial engineering students to give the fundamental concepts of queuing theory and discrete systems simulation. The course provides statistics and probability concepts used in simulation, design of discrete systems simulation models, programming of simulation models, input modeling, random number generation and output analysis.

 

This course provides a comprehensive presentation of the analytical approaches for modeling manufacturing systems. The main purpose of the course is to understand the key factors affecting the performance of manufacturing systems, and develop models for optimizing the performance of these systems. The analytical modeling approach is mainly focused on queuing based probabilistic models. The course covers a review of fundamental concepts from probability and queuing theory together with single-stage systems, multi-stage single product, multi-product systems, batching, workload controlled systems, and kanban controlled manufacturing systems.

 

The aim of the course is to introduce the graphical models used in decision analysis and to provide a set of systematic tools to help the decision maker in giving a decision.

The students who have succeeded in this course; Recognize the graphical models used in decision analysis. Model a given uncertain situation with Bayes networks. Compute exact and approximate inferences in Bayes networks. Model a given uncertain decision problem with influence diagrams. Make inferences in decision networks. Compute value of information.

 

This course introduces the lean production principles and practice. Industrial and production engineers are responsible for continuously improving operational performance. They must develop systems that are fast, flexible, focused and friendly for their companies, customers and production associates. The course provides the student with an introduction to lean production describing the background behind its development. Lean production tools and techniques is described. Planning for lean process implementation and the necessity of sustain improvements is discussed. Examples of applications in manufacturing and business processes is presented.

 

This course provides the fundamental concepts of product development in a managerial and operational context. Design concepts and techniques are introduced as well as strategies and approaches to innovation. The management of innovation processes for the development of sustainable products is also emphasized.

 

The main aim of this course is to give the students an overview of important areas where optimization problems often are considered, and an overview of some important practical techniques for their solution. Another purpose of this course is to provide insights into such problem areas from both an application and theoretical perspective, including the analysis of an optimization model and suitable choices of solution approaches. The students who have succeeded in this course; Understand the main principles behind the modeling of optimization problems, have a clear overview of the most important classes of optimization problems, understand at least one basic solution algorithm to solve each optimization problem class, understand the logic behind the optimization solvers and use them.

 

This course aims to build up the multi-attribute decision making concept by defining the decision making problem with its various constituents. The students who have succeeded in this course;Understand the nature of decision making,Structure and model multi-attribute decision making problems, Select the most appropriate method to solve multi-attribute problems, Integrate the preferences of a group of decision makers, Solve real world problems having conflicting criteria. The course includes various multi-attribute decision making tools and techniques such as elementary methods, value based methods, and outranking methods in order to help industrial engineering students to handle real-life problems with a scientific approach.

 

This course aims to introduce students modeling of linear and integer programs, network flow problems and nonlinear programs; to use the simplex algorithm for solving liner programming problems, branch&bound for solving integer programming problems and some solution algorithms for network flow problems; to understand important modeling techniques and solution algorithms; to get insights about graph theory and its applications; and to identify the types of problems and their solution algorithms.This course emphasizes modeling of problems as linear programs, mixed integer linear programs, nonlinear programs and network flow programs. In the second half of the course some basic solution algorithms such as simplex and branch and bound, and some network flow programming algorithms are covered.