The Electronics and Computer Engineering (ECE) Master's Program was designed for students with backgrounds in electronics engineering and computer sciences. The experience of the past 2 decades have shown that complex systems from the small (smart cards, mobile phones) to the large (Internet, networked storage, server farms) can be only designed and developed by teams of electronics engineers and computer scientists, since they consist of hardware and software components that have to interact and cooperate with one another. Such systems are now the driving forces of our daily lives, from medical technology to Internet banking.
The aim of the ECE MS Program at Sehir is to provide our graduates the necessary and sufficient scientific and technical backgrounds, hardware and software design tools, and methodical approaches in hardware/software codesign. Our graduates will have indepth knowledge and dexterity in design tools, and will be able to specialize not only in classical areas of electronics engineering and computer science, but also in mathematical, computational, physical, and life sciences.
The ECE Master's program can be completed in oneyear (2 academicyear terms and one summer term), and all accepted students will be offered scholarships.
ADMISSION REQUIREMENTS
Graduate Programs in the GSNAS are for students with an undergraduate degree (UG) in engineering, technology, sciences (such as physics and mathematics) and business/economics. Students with a different background and/or have not completed coursework listed below may be required to take some additional courses in the scientific preparation program:
ADMISSION CRITERIA
Admission to the MS programs depend on
APPLICATION – REGISTRATION CALENDAR
Application to Graduate Programs can be made yeararound at www.sehir.edu.tr/graduate
Application deadlines are announced in Academic Calendar pages. But the following dates can be taken as guidelines:
Application Deadlines for programs with thesis option:
Application Deadlines for programs without thesis option:
ONLINE APPLICATION
*This page is active at Mozilla Firefox 3.x and previous versions, Internet Explorer 8 and previous versions and Google Chrome. If you are using Internet Explorer 9, it must be operated in compatibility mode.
REGISTRATION
Admitted students should come in person and register into the Graduate Program with the originals of the following documents (Originals of all uploaded documents during online application will need to be submitted during registration. Failing to do so will result in denial of registration)
SCHOLARSHIPS and FINANCIAL AID
İstanbul Şehir University Placement Exam and STEPSehir Test of English Proficiency
All candidates, who want to pursue graduate studies at İstanbul Şehir University but do not have proof of a language proficiency score are subject to the exams below:
1. Placement Exam: All candidates, who cannot prove their English proficiency, are required to take a Placement Exam. As a result of this exam which is given free of charge, all candidates at and over intermediate level gain the right to take the STEPEnglish proficiency exam. Students who are at the intermediate level and who fail the STEP exam, are provided the means to improve their language skills at the English preparatory program of İstanbul Şehir University, within a very limited quota and upon approval of the related graduate program. Candidates who score under the intermediate level are deemed unsuccessful and not taken into consideration by the graduate program.
The following exams are only for Graduate School of Natural and Applied Sciences students:
The exam will be assessed according to the predetermined standards of İstanbul Şehir University’s testing unit.
Speaking exam will be on appointment basis. Candidates will receive the speaking schedule in the morning, during the written section of STEP.
The exam consists of four sections: Reading, Listening, Writing, and Speaking.
Reading is worth 35% of the overall mark, and approximately takes 80 minutes. This section consists of three texts. The first text is worth 5% and consists of an average of 1000 words. The candidates are required to answer 5 questions in 10 minutes. This section tests the candidates’ skills in identifying the main idea of a given passage and the major points within that selection. The second and third texts are worth 30% combined; the second text consists of approximately 1000 words and the third text, approximately 2000 words. Candidates will have 70 minutes to complete both the reading of text 2 and 3; there are 710 questions on text 2 and 1220 questions on text 3. Part 2 and 3 of the reading tests the candidates’ skills in identifying major points, reading for details, analyzing a text for coherence, and inferencing and summarizing skills.
Listening is worth 25% of the overall mark and approximately takes 35 minutes. This section consists of two parts. The first part is worth 10% and takes approximately 10 minutes. This part tests the candidates’ ability to answer 810 questions while listening to a conversation. The second part is worth 15% and takes 25 minutes in total to complete. There are 815 questions for this part. Candidates listen to an academic lecture for which they are expected to simultaneously listen and take notes. As they take notes, candidates need to differentiate between the main ideas, major points, and significant details presented; after the lecture, the question sheets are distributed and, in addition to assessing these three skills, candidates may also be asked to make inferences from the information given.
On the first day of the exam, the last section is the writing section. The writing section is worth 20% and takes 70 minutes in total. The first 10 minutes of this period are for the candidates to plan. Candidates then write an argumentative essay of 350 words or more on a given topic related to the second listening (the lecture), for which they are encouraged to use the notes they took during that section as supporting ideas.
The speaking section of the exam consists of 3 parts and is worth 20%. This section takes approximately 1520 minutes for each candidate. In the first part of the speaking section, candidates have a daily conversation of approximately 2 minutes on simple topics with an examiner. For part 2, candidates will be given a written topic on which they should speak in a monologue for approximately 4 minutes. Part 3 is the only portion of the speaking section in which candidates speak with a partner (who is also a candidate). Each candidate will be given one side of an issue or situation to speak on; the time allotted for this third task is 10 minutes, or 5 minutes per candidate. Candidates are notified in advance of the time and place for the speaking section; they must arrive at least 30 minutes prior to the given exam time and cannot arrive late. Each candidate enters the speaking exam room together with another candidate.
Precentages  Reading  %35 
Listening  %25  
Writing  %20  
Speaking  %20 
Reading  Text 1: 5%, approx. 1000 words, 5 questions, 10 minutes 
Text 2: 10%, approx. 1000 words, 710 questions, 70 minutes with Text 3  
Text 3: 20%, approx. 2000 words, 1220 questions, 70 minutes with Text 2  
Total Time: 80 minutes  
Listening  While Listening: 10 %, approx. 10 minutes, 710 questions 
Listening Notetaking: 15 %, approx. 1216 minutes listening and 10 minutes to answer questions, 815 questions.  
Total Time: 3540 minutes  
Writing  Argumentative Essay: 20 %, 10 minutes for planning and 60 minutes to write (70 minutes total), 350+ words 
Speaking  Part 1: Daily Conversation, approx. 2 minutes 
Part 2: Long turn (monologue), approx. 4 minutes  
Part 3: Discussion with partner, approx. 10 minutes (5 minutes each)  
Total Time: Approx. 1525 minutes Total Percentage: 20 % 
Application Conditions for English Placement and Proficiency Exam for Graduate Programs in English
Application dates for these exams are between August 25 and September 1st, 2014. Candidates are required to complete their application procedures no later than 17:00, on Monday, September 1st, 2014.
1. Candidates are required to fill in the application form by 5 pm., on September 1st 2014, at the English Preparatory Program Secretary Çiğdem Koruk’s Office, no.229, on the second floor of İstanbul Şehir University Altunizade East Campus. Two (2) passport size photos are required for the form. This form will also be your certificate of entrance to the exam. Certificate of entrance will be used both in Placement and Proficiency exams. (for those who gain the right to enter the STEP)
2 passport size photos are required for each of the graduate applications and English exam applications.
2. The result of the English Placement Exam will be announced at 1 pm. on September 3rd, 2014. As a result of the placement exam, all candidates at and over intermediate level gain the right to take STEPEnglish proficiency exam. For STEP, you have to pay a test fee. The test fee is 100 TL (VAT included). Applicants may pay the fee of the exam from the announcement of the results of Placement Exam by 9:30 am, September 4th, 2014. Students are required to transfer this amount to the bank account state below by writing their "First Name&Last Name” in the explanation section. Candidates can pay in cash or by credit card at the university. When payment is made by either credit card or cash the candidate is given a receipt of payment by the Financial Affairs Office. The test fee is unrefundable.
Bank: Halkbank
Branch: Suadiye Branch (Code:149)
Account Name: İstanbul Şehir University
Account Name: 16000011
IBAN: TR10 0001 2009 1490 0016 0000 11
Those, who transfer the exam fee from another person’s account, are required to write their “First Name&Last Name” in the explanation. Failure to do so could result in problems for the student on the day of the exam.
3. Candidates must remember to bring the entrance certificate and a photographed identity card (national identity card, student ID card, passport etc.) on the day of the exam. Those, who do not submit a photographed ID card, will not be allowed to take the exam.
Wish you the best of luck.
İstanbul Şehir University
School of Languages
English Preparatory Program
Information about Labs
All the labs in the Industrial, Computer Science and Engineering, Electrical and Electronics Engineering Departments will be available to MS students as well. Following is a short description of these labs.
LAB I : Computer Aided Engineering (CAE) Lab
Several high powered work stations will be available to students for their design, analysis and simulation related research and course work. It will host a number of software as listed below.
Table 1. Computer Aided Engineering (CAE) Lab
Software Seat number
Workstations, desks and printers 20
ANSYS 20
LSDYNA 20
MATLAB 20
SOLIDWORKS 20
Other IE related software (Simul8, BestFIT, SPSS, ARENA, ProModel, AutoMod) 20
LAB II : Computer Aided Manufacturing (CAM) Lab
In this lab, student will be able to experience manufacturing related aspects of ISE. They will apply their results from CAE Lab designs, analyses and simulations into a smallscale but real manufacturing environment. Students will be hosted by engineers and technicians after a short training on safety, machine use and cleaning.
Table 2. Computer Aided Manufacturing (CAM) Lab
Machine/Device Number
Robot, conveyor, support sytem 1
CNC machine (milling and turning) 2
Rapid Prototyping System 1
Metrology and Inspection System 1
LAB III: Microelectronics and Microwave (mEmW) Lab
LAB IV : Photonic and Nanotechnology (PN) Lab
LAB V : Computer Science (CS) Lab
This program offers three tracks. The requirements for graduation differ between the tracks.
TRACK 1: Computer Science
OPTION WITH THESIS
The following program requirements must be completed to earn a MS degree in ECE with Computer Science Track:
OPTION WITHOUT THESIS
The following program requirements must be completed to earn a MS degree in ECE with Computer Science Track without thesis:
CORE COURSES for Track 1 (Computer Science)
Course Code  Course Name  Course Code  Course Name  Course Code  Course Name 

ECE 523  Machine Learning  ECE 571  Data Engineering  ECE 580  Networks 
TRACK 2: Communication and Signal Processing
OPTION WITH THESIS
The following program requirements must be completed to earn a MS degree in ECE with Communication and Signal Processing Track:
OPTION WITH THESIS
The following program requirements must be completed to earn a MS degree in ECE with Communication and Signal Processing Track:
CORE COURSES for Track 2 (Communication and Signal Processing)
Course Code  Course Name  Course Code  Course Name  Course Code  Course Name 

ECE 511  Probability and Stochastic Process  ECE 512  Information Theory  ECE 513  Digital Communication 
ECE 509  Statistical Signal Processing  ECE 515  Linear Dynamical Systems  ECE 507  VLSI Design 
TRACK 3: System and Circuit Design
OPTION WITH THESIS
The following program requirements must be completed to earn a MS degree in ECE with System and Circuit Design Track:
OPTION WITH THESIS
The following program requirements must be completed to earn a MS degree in ECE with System and Circuit Design Track:
CORE COURSES for Track 3 (System and Circuit Design)
Course Code  Course Name  Course Code  Course Name  Course Code  Course Name 

ECE 501  Advanced Analog Integrated Circuit Design  ECE 503  mm Wave IC Design  ECE 507  VLSI Design 
ECE 502  VLSI Design  ECE 504  Advanced Communication Circuit Design  ECE 541  Advanced Nano and Micro Electro Mechanical Systems Electro Mechanical Systems 
NOTE 1: All courses are 3 credits and ECTS=5. ECTS for seminar courses is 10, for project course is 20 and for thesis is 60.
NOTE 2: With the approval of their adviser, students may take courses from other graduate programs at İstanbul Şehir University or other universities.
NOTE 3: With the approval of their adviser, students may take ECE 592 (ECE 592A) Independent Study I and ECE593 (ECE592B) Independent study II to complete their course requirements for MS degree.
NOTE 4: With the approval of their adviser, students may take at most two senior level courses to complete their course requirements for MS degree.
NOTE 5: ECE591A is the prerequisite of ECE591B.
LIST OF CORE AND TECHNIC ELECTIVE COURSES
Course Code  Course Name  Course Code  Course Name  Course Code  Course Name 

ECE 501  Analog Circuit Design  ECE 524  Computer Vision  ECE 564  Computational Geometry 
ECE 502  RF Radio Design  ECE 525  Advanced Topics in Computer Vision  ECE 565  Theory of Computation and Complexity 
ECE 503  mm Wave IC Design  ECE 526  Computational Biology  ECE 570  Software Engineering 
ECE 504  Advanced Communication Circuits  ECE 527  Bioinformatics  ECE 571  Data Engineering 
ECE 507  VLSI Design  ECE 528  Probabilistic Graphical Models  ECE 572  Advanced Topics in Database Systems 
ECE 509  Statistical Signal Processing  ECE 529  Multimedia Systems  ECE 573  Advanced Topics in Database Systems 
ECE 511  Probability and Stochastic Process  ECE 531  Advanced Electromagnetics  ECE 574  Advanced Topics in Computer Architecture 
ECE 512  Information Theory  ECE 536  Fiber Optic Communications  ECE 575  Big Data Analysis 
ECE 513  Digital Communication Systems  ECE 537  Optoelectronic Devices and Lasers  ECE 576  Scalable Internet Services 
ECE 145  Data Compression and Modelling  ECE 538  Optical Waves and Optical Imaging  ECE 577  Cloud Computing 
ECE 515  Linear Dynamical Systems  ECE 541  Advanced Nano and Micro Electro Mechanical Systems  ECE 578  Data Visualization 
ECE 165  Convex Optimizations  ECE 542  Advanced MOSFET Theory  ECE 580  Networks 
ECE 517  Numerical Methods in Engineering  ECE 551  Design and Manufacturing in Electrical Engineering  ECE 581  Network Security 
ECE 519  Robotics  ECE 552  Energy and Enegy Efficiency  ECE 582  Data Science for Bussiness 
ECE 521  Motion Planning  ECE 561  Approximation Algorithms  ECE 583  Cryptography and Coding 
ECE 522  Antenna Theory and Design  ECE 562  Network Modelling  ECE 584  Advanced Cryptography and Coding 
ECE 523  Machine Learning  ECE 563  Combinatorial Algorithms  ECE 590  Advanced Topics in Electrical Engineering 
ECE 532  Mobile Programming 
ECE501 Analog Circuit Design
Description: The course deals with the analysis and design of switched capacitor circuit, Nyquist rate and oversampling digitaltoanalog and analogtodigital converters and continuous time filters. The emphasis will be at the opamp level and above; however, transistor level knowledge is required. A detailed summary on transistor level design will be provided as well.
Prerequisites: A course on electronic circuits (MOS Transistors) and discretetime signal processing is required. Knowledge on continuous time filters is desirable.
Textbook: Analog Integrated Circuit Design by Tony Chan Carusone, David A. Johns, Ken W. Martin
ECE502 RF Radio Design
The course provides thorough introduction to the fundamental concepts of RF design, including nonlinearity, interference and noise. Modulation and detection theory; multiple access techniques, and circuits pertinent to current wireless networks. It includes case studies of transceiver architectures from leading manufacturers. The course also involves CAD activity.
ECE503 mm Wave IC Design
The course focuses on silicon based technologies and covers device modeling, circuit building blocks, phased array systems, and antennas pertinent to mmWave amplifiers, mixers, VCO’s, power amplifiers, and beam forming arrays.
ECE504 Advanced Communication Circuits
The course covers a brief review of analog and digital communication concepts, contemporary receiver transmitter architectures for modern wired and wireless communication systems, delaylocked loops (DLLs), fractionalN synthesizers, phase locked loops and clock recovery circuits. The course involves design and CAD activity.
ECE507 VLSI Design
This course provides an introduction to the design of digital ASICs and microprocessors. Students will be introduced to the various steps in design process including planning, design and verification.
ECE509 Statistical Signal Processing
This course provides the students with a solid background with the theories of estimation and detection. The course introduces the concepts of consistency and bias in estimation, criteria for the parameter estimation, performance bounds for the estimation problems, and the ability to assess the performance of the estimation techniques. The course then introduces some of the estimation techniques: linear estimators, ML, techniques based on Bayesian estimation, and Kalman estimators. The course then presents the basics of detection theory. This course is a must for students who wish to acquire a deep understanding of the Fundamentals of parameter estimation and detection; the techniques that are used extensively in communication systems.
Textbook: Fundamentals of Statistical Signal Processing, Volume 1: Estimation Theory and Volume 2: Detection Theory, By Steven Kay, 2009.
ECE511 Probability and Stochastic Process
This course covers the introduction to probability and random processes relevant to electrical and electronics engineering applications. Topics include probability axioms, sigma algebras, random vectors, expectation, probability distributions and densities, Poisson and Wiener processes, stationary processes, autocorrelation, correlations and spectra, spectral density, effects of filtering, linear leastsquares estimation, and convergence of random sequences.
ECE512 Information Theory
Our course will explore the basic concepts of Information theory for students planning to delve into the fields of communications, data compression, and statistical signal processing. It will cover the concepts of source, channel, rate of transmission of information. Entropy and mutual information. The noiseless coding theorem. Noisy channels; the coding theorem for finite state zero memory channels. Channel capacity. Error bounds. Parity check codes. Source encoding.
ECE513 Digital Communication Systems
This is an advanced course that covers digital transmission of information across discrete and analog channels. It covers up to date subjects such as sampling; quantization; noiseless source codes for data compression: Huffman's algorithm and entropy; block and convolutional channel codes for error correction; channel capacity; digital modulation methods: PSK, MSK, FSK, QAM; matched filter receivers. Performance analysis: power, bandwidth, data rate, and error probability.
ECE514 Data Compression and Modeling
This course covers the introduction to a variety of source coding techniques such as quantization, block quantization; and differential, predictive, transform and tree coding. Introduction to ratedistortion theory. Applications include speech and image coding.
ECE515 Linear Dynamical Systems
Course offers an introduction to applied linear algebra and linear dynamical systems with applications to circuits, signal processing, communications, control systems and autonomous dynamical systems.
ECE516 Convex Optimizations
This course covers different optimization tools and algorithms such as: Genetic algorithms, ant colony optimization, tabu search, integer programming, and evolutionary algorithms. It also covers optimal control and dynamic programming; linear quadratic regulator, Lyapunov theory and methods, timevarying and periodic systems, realization theory, linear estimation and the Kalman filter. Examples and applications from digital filters, circuits, signal processing, bioinformatics and control systems.
ECE517 Numerical Methods in Engineering
Basic methods for obtaining numerical solutions with a digital computer will be discussed. Included are methods for the solution of algebraic and transcendental equations, simultaneous linear equations, ordinary and partial differential equations, and curve fitting techniques. The methods are compared with respect to computational efficiency and accuracy.
ECE519 Robotics
This is a course on modeling, design, planning and control of robot systems. It surveys results from geometry, kinematics, statics, dynamics and control theory.
ECE521 Motion Planning
This course provide coherent framework of motion planning for robots and autonomous vehicles and it covers subjects pertinent to automatic motion planning such as path planning, space configuration, sampling strategies, target detection and tracking and collision detection.
ECE522 Antenna Theory and Design
Description: The objective of the course is to provide an overview of antennas and wave propagation for wireless communications. The course will cover fundamentals of radiation and propagation, antenna parameters, simple radiating systems, linear wire, loop, and broadband antennas, antenna arrays, and antenna synthesis.
ECE 523 Machine Learning
In this course, students learn advanced programming techniques for representing and reasoning about complex objects and various applications of such techniques, including expert systems, natural language processors, image understanding systems and machine learning.
ECE524 Computer Vision
The goal of computer vision is to compute properties of the threedimensional world from digital images. Problems in this field include identifying the 3D shape of an environment, determining how things are moving, and recognizing familiar people and objects, all through analysis of images and video. This course provides an introduction to computer vision, including such topics as feature detection, image segmentation, motion estimation, image mosaics, 3D shape reconstruction, and object recognition.
ECE 525 Advanced Topics in Computer Vision
This class will focus on advanced topics in computer vision: image sequence analysis, spatiotemporal filtering, camera calibration and handeye coordination, robot navigation, shape representation, physicallybased modeling, regularization theory, multisensory fusion, biological models, expert vision systems, and other topics selected from recent research papers.
ECE526 Computational Biology
This course focuses on algorithms derived from computer science such as robotics, computational geometry to study structure and motion of molecules.
ECE527 Bioinformatics
This course is an algorithmic principles driving in bioinformatics. It emphasizes the relatively few design techniques used in diverse range of practical problems in biology such as DNA mapping, genome rearrangements, statistical methods for gene prediction and molecular evolution.
ECE 528 Probabilistic Graphical Models
This class covers both the theoretical underpinnings of the PGM framework and practical skills needed to apply these techniques to new problems. In particular this class will teach the basics of the Probabilistic Graphical Models (PGM) representation and how to construct them, using both human knowledge and machine learning techniques; algorithms for using a PGM to reach conclusions about the world from limited and noisy evidence, and for making good decisions under uncertainty. This class is based on a class offered at Stanford University.
ECE 529 Multimedia Systems
The course topics include multimedia systems, applications, and standards for video, image, audio, haptics, and other media. Specifically, multimedia networking, transport, and adaptation, (ii) multimedia compression, coding, and processing, (iii) multimedia synchronization, quality of service, and resource management are included. Furthermore, case studies in multimedia conferencing, gaming, and mobile multimedia are covered.
ECE531 Advanced Electromagnetics
Detailed discussion of electromagnetic theory and wave analysis in various media. It also includes the theory of transmission lines and basics of microwave circuits
ECE 532 Mobile Programming
Description: This course will introduce programming modern android apps for smartphones and tablets. A complete and useful app will be developed that uses programming tools that Android software developers use.
ECE536 Fiber Optic Communications
Overview of optical communication networks and building blocks of optical communication systems.
ECE537 Optoelectronic Devices and Lasers
Course covers the fundamental theory of semiconductor optoelectronic materials and lasers.
ECE538 Optical Waves and Optical Imaging
Course covers the wave theory in optics and its application to imaging and spectroscopy.
ECE541 Advanced Nano and Micro Electro Mechanical Systems
The objective of this course is to provide the design and operational principles of micro/nanoelectromechanical devices and systems (M/NEMS). The course will cover scaling laws, overview of micro and nanofabrication methods including topdown and bottomup approaches, M/NEMS sensors and actuators, micro/nanofluidics, and applications of M/NEMS in electronics, photonics, sensing, and biomedical fields.
ECE542 Advanced MOSFET Theory
This course offers through understanding of MOSFET operation and physical limits of future technology.
ECE551 Design and Manufacturing in Electrical Engineering
This course teaches contemporary issues in circuit design, optical systems, microwave systems, communications and biotechnology.
ECE552 Energy and Energy Efficiency
This course introduces the methods and issues related to energy production, distribution and its efficient use.
ECE 561 Approximation Algorithms
This is a core theory course. We will discuss a wide array of fundamental topics that include Epsilon approximations, PTAS and FPTAS; techniques for the design of approximation algrorithms; P, NP, NPcomplete problems, polynomial transformations, Turing reductions, strong NPcompleteness, NPhardness and inapproximability results; topics in algorithms include: amortized analysis, advanced graph algorithms and data structures.
ECE 562 Network Modeling
Prerequisites: No strict prerequisite. Some statistical and stochastic processes background may be useful. Necessary fundamentals will be reviewed in the class.
A course on network modeling and analysis of complex systems from natural ones such as biological networks, food webs etc. to manmade networks such as the Internet, communication networks, peertopeer networks, sensor networks, networksona chip, power grid, etc. and social networks such as acquaintance networks, organizational networks, online communities etc. In this course the main topic is using the graphtheoretical approaches.
ECE 563 Combinatorial Algorithms
Many largescale scientific discoveries are enabled by combinatorial algorithms. The course focuses on the recent trends on the boundary of combinatorial algorithms and scientific computing. Methods for solving sparse linear systems (direct and iterative), graph models for matrix factorizations, linear algebraic formulations of graph algorithms, graph/hypergraph partitioning, matching, and graph coloring for finite differences.
ECE 564 Computational Geometry
The purpose of this course is to present and discuss algorithms and lower bound techniques in computational geometry; decision tree models of computation; geometric searching; point location and range search; convex hull and maxima of a point set; proximity algorithms; geometric intersections.
ECE 565 Theory of Computation and Complexity
Advanced subjects in computation theory.
ECE 570 Software Engineering
In this course, students learn the principles of software engineering disciplines emphasizing requirements analysis, specification design, coding, testing and correctness proofs, maintenance, and management. Students use a number of software engineering tools.
ECE 571 Data Engineering
This course covers stateoftheart and stateofthepractice activities in the knowledge and data engineering area. We are interested in welldefined theoretical results and empirical studies that have potential impact on the acquisition, management, storage, and graceful degeneration of knowledge and data, as well as in provision of knowledge and data services. Specific topics include, but are not limited to: a) artificial intelligence techniques, including speech, voice, graphics, images, and documents; b) knowledge and data engineering tools and techniques; c) parallel and distributed processing; d) realtime distributed; e) system architectures, integration, and modeling; f) database design, modeling and management; g) query design and implementation languages; h) distributed database control; j) algorithms for data and knowledge management; k) performance evaluation of algorithms and systems; l) data communications aspects; m) system applications and experience; n) knowledgebased and expert systems.
ECE 572 Advanced Topics in Database Systems
In this course, we cover data models, semantics, data integrity, database design, serializability theory, concurrency control, recovery, distributed databases.
ECE 573 Advanced Topics in Distributed Systems
In this course, we cover the fundamental problems in distributed systems and the various tools used to solve them. Of primary interest is the issue of faulttolerance. Topics include event ordering, clocks, global states, agreement, faulttolerance, and peertopeer systems.
ECE 574 Advanced Topics in Computer Architecture
In this graduate course, students learn the advanced instruction set architectures, pipelining, dynamic scheduling, branch prediction, superscalar issue, outoforder execution, memoryhierarchy design, advanced cache architectures and prefetching. As part of the class, several real designs are dissected and simulators are developed for performing quantitative evaluations of design decisions
ECE 575 Big Data Analysis
The course will discuss algorithms for analyzing very large amounts of data. The emphasis will be on Map/Reduce (M/R) as a tool for creating parallel algorithms that can process very large amounts of data. Newly emerging massive data analysis and processing tool stack including Spark, Shark, Storm, Tachyon, and MLBase will be used extensively to work with big data rapidly.
ECE 576 Scalable Internet Services
In this course, students learn about all the technologies that go into a scalable internet service, specifically into dynamic web sites. A very handson, learnbydoing course with a significant project component. Building a transactional dynamic web site in Ruby on Rails and running on Amazon's Elastic Compute Cloud (EC2). Deployment on multiple servers on EC2 and using httperf to demonstrate that the site scales by running a frontend load balancer server, a database server, a memcached server, and up to 10 application servers.
ECE 577 Cloud Computing
Students gain practical knowledge in growing technology industry. Cloud computing refers to a network that distributes processing power, applications and large systems among many computers. The “Cloud Computing” course will provide students with current industry techniques and practices, outline future challenges and survey applications deployed by Amazon, Google and Microsoft. Through the exploration of these services, participants will build an understanding of cloud computing models, techniques and architectures, and its application by providers in delivering common business functions such as data storage, computing resources and messaging online.
ECE 578 Data Visualization
The increasing scale and accessibility of digital data – including government records, corporate databases, and logs of online activity – provides an underexploited resource for improving governance, business, academic research, and our personal lives. For such data to prove broadly useful, people from a variety of backgrounds must be able make sense of it. Facilitating the analysis of large and diverse data sets is a fundamental challenge in both computer systems and humancomputer interaction research, and requires the design of new tools for exploring, analyzing and communicating data.
This course will explore how a broad class of data analysts might more effectively work with data through novel interactive tools. The class will be interdisciplinary in nature, therefore we will touch on diverse topics such as data management (analytic databases, text analysis), user interface techniques (programmingbydemonstration, visualization), and humancentered issues (perceptual, cognitive and social factors).
ECE 580 Networks
Over the past decade there has been a growing public fascination with the complex "connectedness" of modern society. This connectedness is found in many incarnations: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity. These are phenomena that involve networks, incentives, and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which each of our decisions can have subtle consequences for the outcomes of everyone else.
This course combines different scientific perspectives in its approach to understanding networks and behavior. Drawing on ideas from economics, sociology, computing and information science, and applied mathematics, it describes the emerging field of study that is growing at the interface of all these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected.
ECE 581 Network Security
In this course, we study the theoretical and practical aspects of network security. We start with a threat model, and describe vulnerabilities of computer networks to attacks by adversaries and hackers using a variety of techniques. We then study methods and techniques to circumvent or defend against these attacks and to minimize their damage. In this context, we study cryptographic techniques and protocols, network security protocols, digital signatures and authentication protocols, network security practice, and wireless network security.
Security attacks, mechanisms, and services; network security and access security models; overview of secretkey and publickey cryptography; authentication protocols and key management; network security practice; email security; IP security and web security; intrusion detection and prevention systems; firewalls and virtual private networks; wireless network security.
ECE 582 Data Science for Business
Data Science for Business introduces the fundamental principles of data science, and walks you through the "dataanalytic thinking" necessary for extracting useful knowledge and business value from the data you collect. The class provides examples of realworld business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how to participate intelligently in your company’s data science projects. You’ll also discover how to think dataanalytically, and fully appreciate how data science methods can support business decisionmaking.
ECE 583 Cryptography and Coding
The first part of the course concentrates on methods, algorithms, techniques, and tools of cryptography. We study in detail algorithmic and mathematical aspects of cryptographic methods and protocols, such as secretkey cryptography, publickey cryptography, hash functions, and digital signatures. We also show how these techniques are used to solve particular data and communication security problems. The second part of the course deals with subjects related to algebraic codes, their constructions, mathematical properties, and encoding and decoding algorithms.
ECE 584 Advanced Cryptography and Coding
This graduate course is designed for computer science, mathematics, electrical engineering students interested in understanding, designing, developing, testing, and validating cryptographic software and hardware. We will study algorithms, methods, and techniques in order to create stateofart cryptographic embedded software and hardware using common platforms and technologies.
ECE 590 Advanced Topics in Electrical Engineering
Advanced subjects and problems in electrical and computer engineering will be studied.
ECE591A/591B Graduate Seminar
In this course, seminars are offered by faculty, guest speakers and graduate students about various electrical engineering topics, aiming to broaden students' interest and vision. Scientific research methods are presented. Each student is expected to study a selected topic in greater detail and submits a report at the end of the semester. A presentation to the class completes the work. The grade will be Satisfactory (S) or Failure (F).
ECE591A is the prerequisite of ECE591B.
From the rise of Internet to this day, the Web has evolved from being a one way communication environment to manytomany communication and collaboration environment. A series of transformations has led to today’s Web. In the 1990s, the Web served lots of static HTML pages created by a small set of people at select institutions and news agencies. From the beginning of 21st century, the number of contributors and the amount of information has skyrocketed with the rise of platforms that enable rapid collaboration and personal contribution. With so much data around, we are riding a wave of the Web intransit from version 2.0 to its next version, Web 3.0. The premise of the newest version of the Web is the welcoming of machines understanding, generating, and consuming information just like any of us can do now.
From 2005 to 2010, the digital universe grew from 130 exabytes (EB) to 800 EB. The digital universe will double every two years from now till 2020. In 2020, it will be 40,000 EB, i.e., 40 trillion gigabytes, which is more than 5,200 gigabytes for every man, woman, and child in 2020. In almost every subsystem currently in use, there is a cyclical process that starts with the (i) acquisition of raw data. It is followed by (ii) processing and transforming of this raw data into information so that we can (iii) drive new insights. With more insights, we are better equipped to (iv) make new and informed decisions. These are the decisions that determine whether customers buy what they need, producers design the right products, city officials deploy the right solutions for bettering urban life, our crops and fields return better yields, and whether we live better. With data playing a central role in advancing civilization, it is appropriate to say that “the data has become the new oil”. The 4step cyclical process we described is shaped by Data Science. The idea is to find interesting ways to visualize and present raw data in such a way that enables rapid insight discovery. Instead of verbose text and raw data, vivid visuals are more powerful and useful in the process of deriving new and actionable insights.
Data scientists are inquisitive: exploring, asking questions, doing “what if” analysis, questioning existing assumptions and processes. Rather than looking at data from a single source, a data scientist will explore and examine data from multiple disparate sources. The data scientist will sift through all incoming data with the goal of discovering a previously hidden insight, which in turn can provide a competitive advantage or address a pressing business problem. A data scientist does not simply collect and report on data, but also looks at it from many angles, determines what it means, then recommends ways to apply the data. Armed with data and analytical results, a toptier data scientist will then communicate informed conclusions and recommendations across the organization.
From clustering and regression, to classification and probabilistic inference, and to data enrichment and visualisation, data scientists need to have a solid foundation in computer science and applications, modelling, statistics, analytics and mathematics. In order to explore exabytes of data and do “whatif” analysis, data scientists require powerful backend systems (data science platforms) to crunch raw data. Furthermore, the platforms have to provide an interactive mode of data analysis, which is required due to the iterative and inquisitive nature of performing data science.
In order to fully grasp the opportunities present in the current environment awash with data, we designed a targeted graduate program for Data Science. Our institutional partnership with IBM will help us better this new graduate program going forward. Besides systems and tooling support, enhancing class experience with guest lectures from IBM personnel expert in the areas pertinent to course content will make sure that practical implications and real problems to solve will always be thought of and taught upfront in the academia.
Admission Requirements and Scholarships
Scores for GPA, GRE/ALES, TOEFL/IELTS; letters of references; and statement of purpose essays are all taken into consideration during the assessment of applications for admission and scholarship
Limited but competitive scholarships are offered after a meritbase assessment of all applications.
Scholarship levels may change from a partial tuitionwaver to full scholarship including monthly stipend, housing & meal support and health insurance.
Students may also obtain additional support from their advisers if they involve in sponsored research projects.
Application Dates: 1 July  15 August 2014
ONLINE APPLICATION FORM
Note: All courses are worth 3 credits.
ECE 571 Data Engineering
This course covers stateoftheart and stateofthepractice activities in the knowledge and data engineering area. We are interested in welldefined theoretical results and empirical studies that have potential impact on the acquisition, management, storage, and graceful degeneration of knowledge and data, as well as in provision of knowledge and data services. Specific topics include basic and advanced techniques for textbased information systems, Boolean and vector space retrieval models, web search including crawling, text/Web clustering, classification, and text mining.
ECE 580 Networks
Over the past decade there has been a growing public fascination with the complex "connectedness" of modern society. This connectedness is found in many incarnations: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity. These are phenomena that involve networks, incentives, and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which each of our decisions can have subtle consequences for the outcomes of everyone else.
This course combines different scientific perspectives in its approach to understanding networks and behavior. Drawing on ideas from economics, sociology, computing and information science, and applied mathematics, it describes the emerging field of study that is growing at the interface of all these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected.
ECE 582 Data Science for Business
Data Science for Business introduces the fundamental principles of data science, and walks you through the "dataanalytic thinking" necessary for extracting useful knowledge and business value from the data you collect. The class provides examples of realworld business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how to participate intelligently in your company’s data science projects. You’ll also discover how to think dataanalytically, and fully appreciate how data science methods can support business decisionmaking.
ECE 578 Data Visualization
The increasing scale and accessibility of digital data – including government records, corporate databases, and logs of online activity – provides an underexploited resource for improving governance, business, academic research, and our personal lives. For such data to prove broadly useful, people from a variety of backgrounds must be able make sense of it. Facilitating the analysis of large and diverse data sets is a fundamental challenge in both computer systems and humancomputer interaction research, and requires the design of new tools for exploring, analyzing and communicating data.
This course will explore how a broad class of data analysts might more effectively work with data through novel interactive tools. The class will be interdisciplinary in nature, therefore we will touch on diverse topics such as data management (analytic databases, text analysis), user interface techniques (programmingbydemonstration, visualization), and humancentered issues (perceptual, cognitive and social factors).
ECE 576 Scalable Systems
In this course, students learn about all the technologies that go into a scalable internet service, specifically into dynamic web sites. A very handson, learnbydoing course with a significant project component. Building a transactional dynamic web site in Ruby on Rails and running on Amazon's Elastic Compute Cloud (EC2). Deployment on multiple servers on EC2 and using httperf to demonstrate that the site scales by running a frontend load balancer server, a database server, a memcached server, and up to 10 application servers.
ECE 575 Big Data Analysis
The course will discuss algorithms for analyzing very large amounts of data. The emphasis will be on Map/Reduce (M/R) as a tool for creating parallel algorithms that can process very large amounts of data. Newly emerging massive data analysis and processing tool stack including Spark, Shark, Storm, Tachyon, and MLBase will be used extensively to work with big data rapidly.
ECE 562 Network Modeling
This course is on the analysis of complex systems from natural ones such as biological networks, food webs etc. to manmade networks such as the Internet, communication networks, peertopeer networks, sensor networks, networksona chip, power grid, etc. and social networks such as acquaintance networks, organizational networks, online communities etc. The main analytical tools used are graphtheoretical approaches.
ECE 522 Elements of Statistical Learning
The meteoric rise in computing power has been accompanied by a rapid growth in the areas of statistical modeling and data analysis. New techniques have emerged for both predictive and descriptive learning that were not possible ten years ago, using ideas that bridge the gap between statistics, computer science and artificial intelligence. In this twopart series we cover many of these new methods, with emphasis on the statistical aspects of their application and their integration with more standard statistical methodology.
Predictive learning refers to estimating models from data with the specific goal of predicting future outcomes, in particular regression and classification models. Regression topics include linear regression with recent advances to deal with large numbers of variables, smoothing techniques, additive models, and local regression. Classification topics include discriminant analysis, logistic regression, support vector machines, generalized additive models, naive Bayes, mixture models and nearest neighbor methods.
ECE 523 Machine Learning
In this course, students learn advanced programming techniques for representing and reasoning about complex objects and various applications of such techniques, including expert systems, natural language processors, image understanding systems and machine learning.
ECE 528 Probabilistic Graphical Networks
This class covers both the theoretical underpinnings of the PGM framework and practical skills needed to apply these techniques to new problems. In particular this class will teach the basics of the Probabilistic Graphical Models (PGM) representation and how to construct them, using both human knowledge and machine learning techniques; algorithms for using a PGM to reach conclusions about the world from limited and noisy evidence, and for making good decisions under uncertainty.
ECE 511 Stochastic Processes, Detection, and Estimation
This course examines the fundamentals of detection and estimation for signal processing, communications, and control. Topics covered include: vector spaces of random variables; Bayesian and NeymanPearson hypothesis testing; Bayesian and nonrandom parameter estimation; minimumvariance unbiased estimators and the CramerRao bounds; representations for stochastic processes, shaping and whitening filters, and KarhunenLoeve expansions; and detection and estimation from waveform observations. Advanced topics include: linear prediction and spectral estimation, and Wiener and Kalman filters.
ECE 515 Linear Dynamical Systems
This course offers an introduction to applied linear algebra and linear dynamical systems with applications to circuits, signal processing, communications, control systems and autonomous dynamical systems.
ECE 516 Convex Optimization
This course focuses on convexity, duality, and convex optimization algorithms, with the aim of developing the core analytical and algorithmic issues of continuous optimization, duality, and saddle point theory, using a handful of unifying principles that can be easily visualized and readily understood.
ECE 517 Numerical Methods in Engineering
Basic methods for obtaining numerical solutions with a digital computer will be discussed. Included are methods for the solution of algebraic and transcendental equations, simultaneous linear equations, ordinary and partial differential equations, and curve fitting techniques. The methods are compared with respect to computational efficiency and accuracy.
ECE 518 System Identification
This course is offered to graduates and includes topics such as mathematical models of systems from observations of their behavior; time series, statespace, and inputoutput models; model structures, parametrization, and identifiability; nonparametric methods; prediction error methods for parameter estimation, convergence, consistency, and asymptotic distribution; relations to maximum likelihood estimation; recursive estimation; relation to Kalman filters; structure determination; order estimation; Akaike criterion; bounded but unknown noise model; and robustness and practical issues.
ECE 512 Information Theory
Our course will explore the basic concepts of Information theory for students planning to delve into the fields of communications, data compression, and statistical signal processing. It will cover the concepts of source, channel, rate of transmission of information, entropy and mutual information, the noiseless coding theorem, noisy channels, the coding theorem for finite state zero memory channels, channel capacity, error bounds, parity check codes, and source encoding.
ECE 527 Bioinformatics
This course is an algorithmic principles driving in bioinformatics. It emphasizes the relatively few design techniques used in diverse range of practical problems in biology such as DNA mapping, genome rearrangements, statistical methods for gene prediction and molecular evolution.
ECE 529 Multimedia Systems
The course topics include multimedia systems, applications, and standards for video, image, audio, haptics, and other media. Specifically, multimedia networking, transport, and adaptation, (ii) multimedia compression, coding, and processing, (iii) multimedia synchronization, quality of service, and resource management are included. Furthermore, case studies in multimedia conferencing, gaming, and mobile multimedia are covered.
Mandatory Courses with No Credit
ECE 590 Project Course
A topic is selected and a project proposal will be prepared by the student with the approval of the academic advisor. The student makes research about the proposal, studies the literature, collects data when needed and submits a project report at the end of the semester. A presentation to the class completes the work. The grade will be Satisfactory (S) or Failure (F).
ECE 591 Graduate Seminar
The course aims at widening students' perspectives through seminars offered by faculty, guest speakers and graduate students about various industrial engineering subjects and scientific research methods. Each student is expected to study a selected topic in greater detail and present its key findings before class. The grade will be Satisfactory (S) or Failure (F).
ECE 599 Master of Science Thesis
Each student is required to research, study, prepare and defend a thesis in order to be able to graduate.
For general purposes, the program students can use the graduate student lab that is open to use for the students of Graduate School of Natural and Applied Sciences. For research purposes, the students will use ondemand virtual machines hosted by Şehir IT.
The industrial partner of the graduate program is IBM Türk. Through IBM's academic initiative program, the program students will get access to numerous software, hardware, educational content, and other educational resources through an online portal.
Software accessible through the online portal:
IBM Websphere, IBM Rational, IBM DB2, IBM Lotus, IBM Tivoli, Cloudscape/Derby, Eclipse & other open source software, Gluecode, Linux, AIX software, Parallel & Cluster computing software and other similar software.
Hardware accessible through the online portal:
IBM iSeries curriculum & IBM zSeries curriculum, with remote access or using stand alone verisons.
Furthermore, the online portal provides training labs and exams, an online support forum, educational content, and other discounts on select cetificates.
Depending on availability and suitability, the program students can gain access to resources provided by the following labs.
(i) Data Science Labs I & II
Situated within an area of 100 square meters, the main research threads in the labs are in the areas of Data Engineering, Data Mining, Data Visualization, Big Data Analysis, Scalable Systems, and Probabilistic Graphical Networks. The members of the lab 3 Faculty members, 8 Graduate students, 3 undergraduate students, 6 funded research projects (1 EC, 4 TÜBİTAK, 1 Industrial) have laid the foundational and visionary infrastructure behind the graduate program being offered.
MS with Thesis:
MS without Thesis:
Suggested Course Plan  

Courses  
Theory  Core  Application 
ECE 522  ECE 571  ECE 527 
ECE 512  ECE 523  ECE 529 
ECE 516  ECE 575  ECE 582 
ECE 515  ECE 580  ECE 520 
ECE 517  ECE 528  
ECE 578 
Required number of courses to take  

w/ Thesis  1 or 2  4 or 5  2 
w/o Thesis  0  6  4 
Total number of courses to take  

w/ Thesis  8 
w/o Thesis  10 
PRINCIPLES OF SCHOLARSHIP PRACTICE IN GRADUATE PROGRAMS
Computer scientists have an important role in designing powerful systems that measure physical output, convert the measurement to data and the data to information, and present this information to the benefit of humanity. In developing the information technologies of the future, the world needs creative and innovative young computer scientists and computer engineers. Faculty members and researchers of the Department of Computer Science and Engineering of İstanbul Şehir University have come together with the awareness of this need.
In our curriculum formed jointly with the Department of Electrical and Electronics Engineering, our aim is to help the students acquire an integral approach to the problems in the field of engineering. At our department, an international education awaits the students, which will prepare them in the best way to solve the computer science and computer engineering problems in the industrial and public domains and especially to a Ph.D. education on a more advanced level.
At the Department of Computer Science and Engineering, founded on intellectual and academic freedom, students will learn to take risks and be open to brave initiatives. The research fields in our department include distributed computing, cryptography, advanced database systems, cloud computing, social media and artificial intelligence. In addition, students will gain exposure to engineering ethics, engineering processes and entrepreneurship, and will gain a foundation that is serious, strong, theoretical and based on practice. They will graduate from the Department of Computer Science and Engineering with knowledge and skills to make abstraction and analysis, with a high capacity for design and solution.
MATH 103 Calculus I  Differential (3+2) 4 – ECTS = 5
Differential calculus including analytic geometry; functions, limits and continuity; derivatives, techniques and applications of differentiation; logarithmic and trigonometric functions.
Textbook: Thomas’ Calculus, Maurice D. Weir, Joel Hass, and Frank R. Giordano, Pearson
MATH 104 Calculus II – Integral (3+2) 4 – ECTS = 5
Integral calculus including definite and indefinite integrals; techniques of integration; applications in mathematics and engineering; infinite series. (Prerequisite: MATH 201 or consent of instructor).
Textbook: Thomas’ Calculus, Maurice D. Weir, Joel Hass, and Frank R. Giordano, Pearson.
PHYS 103 Physics I  Mechanics and Dynamics (3+2) 4 – ECTS = 4
Introduction to classical mechanics for students in engineering and the physical sciences. Measurement, units, and foundations of physics; vectors; kinematics; circular motion; forces, mass, and Newton's laws; center of mass; momentum; work and energy; conservation laws; collisions; rotational kinematics.
Textbook: Physics for Scientists and Engineers, Fishbane et. al., 2005. AddisonWesley.
PHYS 103L Physics I – Lab: Mechanics and Dynamics (0+2) 1 – ECTS = 1
Lab work on foundations of physics; vectors; kinematics; circular motion; forces, mass, and Newton's laws; center of mass; momentum; work and energy; conservation laws; collisions; rotational kinematics.
PHYS 104 Physics II  Electromagnetics and Moderns Physics (3+2) 4 – ECTS = 4
Rotational dynamics and angular momentum; equilibrium and elasticity; periodic motion including LRC electrical circuits; gravitation; fluid mechanics; temperature; thermal expansion; heat and the first law of thermodynamics; heat conduction; kinetic theory of gases; entropy and the second law; heat engines. (Prerequisite: PHYS 203 or consent of instructor).
Textbook: Physics for Scientists and Engineers, Fishbane et. al., 2005. AddisonWesley.
PHYS 104L Physics II – Lab: Electromagnetics and Moderns Physics (0+2) 1 – ECTS = 1
Lab work on rotational dynamics and angular momentum; equilibrium and elasticity; periodic motion including LRC electrical circuits; gravitation; fluid mechanics; temperature; thermal expansion; heat and the first law of thermodynamics; heat conduction; kinetic theory of gases; entropy and the second law; heat engines. (Prerequisite: PHYS 203 or consent of instructor).
LIFE 101 Life Sciences I Biology (3+0) 3 – ECTS = 4
Fundamentals of living creatures, cell structures, bio system; its relation with human activities.
LIFE 102 Life Sciences II Chemistry (3+0) 3 – ECTS = 4
Basics of matters, elements and their compound under different circumstances. Its relation and mechanics in human body and surroundings.
ENGR 106 Introduction to Engineering (Noncredit) – ECTS = 2
Introduction to the art and science of engineering through the basics of mechanical, electrical, industrial and computer systems. Topics covered include: definition and history of engineering; ethics and social responsibility; engineering design process; engineering solutions; estimations and approximations; dimensions; units and conversions; mathematics and computer solutions; life long learning; mechanics; energy and thermal sciences; electricity and electronics; materials; manufacturing; economics; statistics, and introduction to the various engineering disciplines and mechanical engineering subdisciplines.
MATH 205 Linear Algebra and Differential Equations (3+0) 3 – ECTS = 5
Linear algebra including systems of linear equations; matrices, inverses of matrices; determinants; vector spaces and subspaces, bases and dimension. First order differential equations, including direction fields, separation of variables, first order linear equations, growth and decay, nonlinear models.(Prerequisite: MATH 201 or consent of instructor).
Textbook: Modern Engineering Mathematics, Glyn James, 2008, Prentice Hall.
ENGR 211 Introduction to Programming (2+2) 3 – ECTS = 5
This course will provide a handson introduction to programming using Python with little or no prior experience in programming computers. The course will focus on planning and organizing programs, as well as the grammar of the Python programming language. Lectures will be interactive featuring inclass exercises with lots of support from the course staff. More advanced concepts in computer programming and software development will be introduced in the later stages of the course. The overarching goal in this course is to build an Engineer mindset in preparation for the upper level courses in EECS department curriculum.
Textbook: http://greenteapress.com/thinkpython/
ENGR 212 Programming Practice (2+2) 3 – ECTS = 5
This course will get the student acquire handson skills while doing advanced programming using Python and Django. Advanced concepts in computer programming and software development (Internet Systems and Web framework) will be introduced in the beginning stages of the course. The overarching goal in this course is to build an Engineer mindset in preparation for the upper level courses in EECS department curriculum.
Textbook:
1. Dive into Python, http://www.diveintopython.net/toc/index.html
2. Mining the Social Web, http://shop.oreilly.com/product/0636920010203.do
3. The Django Book, http://www.djangobook.com/
ENGR 251 Probability for Engineers (3+0) 3 – ECTS = 5
Collection, organization and presentation of data. Introduction to probability theory, counting theorems, conditional probability and independence. Random variables, expectation, discrete probability models, continuous probability models, normal and related distributions. Sampling distributions, central limit theorem. Point and interval estimation. (Prerequisite: MATH 201 or consent of instructor)
Textbook: Applied Statistics and Probability for Engineers 4E, Douglas C. Montgomery and George C. Runger, John Wiley High Education, 2006.
EECS 201 Introduction to EECS I (3+2) 4 – ECTS = 6
Description: An integrated introduction to electrical engineering and computer science, taught using substantial laboratory experiments on devices with sensing capabilities. Key issues in the design of engineered artifacts operating in the natural world: measuring and modeling system behaviors; assessing errors in sensors and effectors; specifying tasks; designing solutions based on analytical and computational models; planning, executing, and evaluating experimental tests of performance; refining models and designs. Issues addressed in the context of computer programs, control systems, probabilistic inference problems, circuits and transducers, which all play important roles in achieving robust operation of a large variety of engineered systems.
Textbook: http://mit.edu/6.01/mercurial/fall10/www/handouts/readings.pdf
EECS 202 Introduction to EECS II (3+2) 4 – ECTS = 6
Description: This lecture is designed as an introductory digital logic design and microprocessor course. Topics covered by this course includes: Binary and nonbinary systems, Boolean algebra, digital design techniques, logic gates, logic minimization, standard combinational circuits, sequential circuits, flipflops, synthesis of synchronous sequential circuits, PLAs, ROMs, RAMs. Principles of hardware and software microcomputer interfacing; Experiments with specially designed laboratory facilities. Assembly language programming. (Lecture and laboratory).
EECS 214 Unix Operating System (1+2) 2 – ECTS = 4
Description: This course is to provide students with experience with a variety of UNIX utilities, the UNIX shell command language, and facilities for managing directories on multiple computers, implementing a personal database, reformatting text, and searching for online resources. In the process of learning the UNIX operating system, they will be applying knowledge of mathematics, science and engineering. Topics covered include use of several utilities (comm, cut, head, tail, sort, tr, wc) alone and in combination, customization of one’s .login and X initialization files, navigation through the UNIX file directory and through various online documentation resources, job control, shell scripting, network use (ssh, scp, rlogin, rcp, rsh, telnet, ftp, mail) and web browsing, using and comparing compression and archiving utilities, sed used in combination with shell scripts, regular expressions, make, find, and awk.
Textbook: Your UNIX: The Ultimate Guide, 2nd edition, by Sumitabha Das. MacGrawHill, 2006.
EECS 216 Signals and Systems (3+0) 3 – ECTS = 5
Description: Covers the fundamentals of signal and system analysis, with applications drawn from filtering, audio and image processing, communications, and automatic control. Topics include convolution, Fourier series and transforms, sampling and discretetime processing of continuoustime signals, modulation, Laplace and Ztransforms, and feedback systems.
Textbook: Signals and Systems, 2nd edition, by Oppenheim, A. V., and A. S. Willsky, with H. Nawab. PrenticeHall, 1997.
CS 351 Computer Architecture (3+0)3  ECTS=6
Description: Lectures and labs illustrate how to build a multicore computer system. Topics include parallelism, instructionset architecture, memory hierarchy, and communication primitives. Using a fieldprogrammable gate array (FPGA) board, programmed with a simple multicore processor and a minimal software environment, students develop Verilog and software to implement different hardware/software designs for caches, messages, shared memory, and coordination primitives. The labs culminate in a term project which students describe in a design paper and inclass presentation. Provides instruction in written and oral communication.
Textbook: http://web.mit.edu/course/6/6.173/
CS 352 Computer Systems (3+0)3  ECTS=6
Description: Topics on the engineering of computer software and hardware systems: techniques for controlling complexity; strong modularity using clientserver design, operating systems; performance, networks; naming; security and privacy; faulttolerant systems, atomicity and coordination of concurrent activities, and recovery; impact of computer systems on society. Case studies of working systems and readings from the current literature provide comparisons and contrasts. Two design projects. Students engage in extensive written communication exercises.
Textbook: Principles of Computer System Design: An Introduction, by Jerome H. Saltzer and M. Frans Kaashoek. Morgan Kaufmann, 2009.
CS 361 Software (3+0)3  ECTS=5
Description: This course introduces concepts and techniques relevant to the production of large software systems. Students are taught a programming method based on the recognition and description of useful abstractions. Topics include: modularity; specification; data abstraction; object modeling; design patterns; and testing. Several programming projects of varying size undertaken by students working individually and in groups.
Textbook: Program Development in Java™: Abstraction, Specification, and ObjectOriented Design, by Liskov, Barbara, and John Guttag. AddisonWesley, 2000.
CS 362 Machine Intelligence (3+0)3  ECTS=5
Description: This course introduces students to the basic knowledge representation, problem solving, and learning methods of machine intelligence. Upon completion of this course, students should be able to: develop intelligent systems by assembling solutions to concrete computational problems, understand the role of knowledge representation, problem solving, and learning in intelligentsystem engineering, and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.
Textbook: Artificial Intelligence, 3rd edition, by Winston, Patrick H. AddisonWellsley, 1992.
CS 371 Data Structures and Algorithms (3+0)3  ECTS=5
Description: This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.
Textbook: Introduction to Algorithms, 2nd Edition, by Cormen, Leiserson, Rivest, and Stein. MIT Press, 2001.
CS 372 Advanced Algorithms (3+0) 3  ECTS=5
Description: This course teaches techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics covered include: sorting; search trees, heaps, and hashing; divideandconquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow; computational geometry; numbertheoretic algorithms; polynomial and matrix calculations; caching; and parallel computing.
Textbook: Introduction to Algorithms, 2nd Edition, by Cormen, Leiserson, Rivest, and Stein. MIT Press, 2001.
ENGR 422  Numerical Analysis for Engineers (3+0) 3 – ECTS = 5
The numerical methods for solving engineering problems are studied. The numerical representation of numbers and the sources of error are discussed. Solution methods for the roots of equations, linear algebraic equations, numerical differentiation and integration, ordinary and partial differential equations, regression and interpolation are studied. Basics in MATLAB are covered and numerical programming applications are offered.
Textbook: Numerical Methods for Engineers, 6th Edition Steven Chapra, Raymond P. Canale, Mc Graw Hill, 2010.
ENGR 497 Global Design Project I (1+2) 2  ECTS=9
Description: Investigation and report on a special project under the direction of a faculty advisor. Involves handling and solving a well defined engineering problem of practical nature fully by applying a synthesis of knowledge and skills acquired in different courses in a particular branch of engineering.
ENGR 498 Global Design Project II (1+2) 2  ECTS=9
Description: Investigation and report on a special project under the direction of a faculty advisor. Involves handling and solving a well defined engineering problem of practical nature fully by applying a synthesis of knowledge and skills acquired in different courses in a particular branch of engineering.
EECS 402 Entrepreneurship (3+0)3  ECTS=5
Description: Successful startups do not follow the traditional productcentric launch model. Through trial and error, hiring and firing, successful startups all invent a parallel process to Product Development. This process is focused on customer learning and discovery, and is called Customer Development. Students learn AtoZ aspects of this radical reexamination of new product introduction process, successful strategies to listen to your customers and addressing their needs head first before product launch, and effective market validation.
Textbook: The Four Steps to the Epiphany, 2nd edition, by Steven G. Blank. Cafepress, 2006.
EECS 403 Digital Circuit Design (3+0) 3 – ECTS = 5
Description: This lecture provides the design and analysis of digital integrated circuits. In particular it focuses on Logic families, comparators, A/D and D/A converters, combinational systems, sequential systems, solidstate memory, largescale integrated circuits, and design of electronic systems. Parasitics, transmissionline effects, packaging. Analog to Digital interfaces. Linear and switching mode power conversion.
EECS 404 Communication Circuits (3+0) 3 – ECTS = 5
Description: The course covers a brief review of analog and digital communication concepts, contemporary receiver transmitter architectures for modern wired and wireless communication systems, delaylocked loops (DLLs), fractionalN synthesizers, phase locked loops and clock recovery circuits. The course involves design and CAD activity.
EECS 405 RF Circuit Design (3+0) 3 – ECTS = 5
Description: The course provides thorough introduction to the fundamental concepts of RF design, including nonlinearity, interference and noise. Modulation and detection theory; multiple access techniques, and circuits pertinent to current wireless networks. The course also involves CAD activity.
EECS 413 Wireless Communications (3+0) 3 – ECTS = 5
Description: Introduction to wireless communications systems. Wireless channel modeling. Single carries, spread spectrum, and multicarrier wireless modulation schemes. Diversity techniques. Multipleaccess schemes. Transceiver design and system level tradeoffs. Brief overview of GSM, CDMA, 3G and other wireless standards.
EECS 415 Digital Signal Processing (3+0) 3 – ECTS = 5
Description: Introduction to discrete Fourier series and transforms. Design of Analog and digital filters. FastFourier transforms, sampling, and modulation for discrete time signals and systems. Consideration of stochastic signals and linear processing of stochastic signals using correlation functions and spectral analysis. Introduction to Matlab tools and matlab based signal processing projects.
EECS 416 Image and Video Processing (3+0)3  ECTS=5
Description: This course is divided into two parts: digital image processing and digital video processing. The first part covers the fundamentals of digital image processing such as image sampling and quantization, color, point operations, segmentation, morphological image processing, linear image filtering and correlation, image transforms, eigenimages, multiresolution image processing, wavelets, noise reduction and restoration, feature extraction and recognition tasks, and image registration. The second part of the course covers the fundamentals of digital video processing, which include spatiotemporal sampling, motion analysis, parametric motion models, motioncompensated filtering, and video processing operations including noise reduction, restoration, superresolution, deinterlacing and video sampling structure conversion, and compression (framebased and objectbased methods).
Textbook:
1. "Video Processing and Communications" by Yao Wang, Joern Ostermann, and YaQin Zhang, Prentice Hall, 2002, ISBN 0130175471.
2. "Fundamentals of Digital Image Processing" by A.K.Jain, PrenticeHall, 1989.
EECS 418 Information Theory (3+0)3  ECTS=5
Description: Information theory is the science of operations on data such as compression, storage, and communication. It is among the few disciplines fortunate to have a precise date of birth: 1948, with the publication of Claude E. Shannon's paper entitled "A Mathematical Theory of Communication". This course will explore the basic concepts of Information theory. It is a prerequisite for research in this area, and highly recommended for students planning to delve into the fields of communications, data compression, and statistical signal processing. The intimate acquaintance that we will gain with measures of information and uncertainty  such as mutual information, entropy, and relative entropy  would be invaluable also for students, researchers, and practitioners in fields ranging from neuroscience to machine learning. Also encouraged to enroll are students of statistics and probability, who will gain an appreciation for the interplay between information theory, combinatorics, probability, and statistics.
Textbook: Elements of Information Theory by Cover and Thomas, 2nd Edition, New York: Wiley, 2006.
EECS 421 Antennas and Propagations (3+0) 3 – ECTS = 5
Description: Basic theory of radiation. Analysis and synthesis of antennas and antenna arrays. Adaptive arrays and digital beam forming for advanced wireless links. Friis transmission formula. Wireless communication equations for cell site and mobile antennas, interference, slow and fast fading in mobile communication.
EECS 423 Optics (3+0) 3 – ECTS = 5
Description: Basic principles of optics: light sources and propagation of light; geometrical optics, lenses and imaging; ray tracing and lens aberrations; interference of light waves, coherent and incoherent light beams; Fresnel and Fraunhofer diffraction. Overview of modern optics.
EECS 424 Photonics and Lasers (3+0) 3 – ECTS = 5
Description: Wave theory of light, optical waveguides and fibers, optical transmission system, heterojunction structures, laser theory, semiconductor lasers, photodiodes and optical detection, photometry and radiometry.
EECS 451 Database Systems (3+0)3  ECTS=5
Description: This course relies on primary readings from the database community to introduce graduate students to the foundations of database systems, focusing on basics such as the relational algebra and data model, schema normalization, query optimization, and transactions. Topics related to the engineering and design of database systems, including: data models; database and schema design; schema normalization and integrity constraints; query processing; query optimization and cost estimation; transactions; recovery; concurrency control; isolation and consistency; distributed, parallel, and heterogeneous databases; adaptive databases; trigger systems; pubsub systems; semi structured data and XML querying.
Textbook:
1. Readings in Database Systems, 4th edition, by Hellerstein, Joseph M., and Michael Stonebraker. MIT Press, 2005.
2. Database Management Systems, 2nd edition, by Ramakrishnan, Raghu, and Johannes Gehrke. McGrawHill, 2000.
EECS 456 Principles of Computer Systems (3+0)3  ECTS=5
Description: This course provides an introduction to the basic principles of computer systems, with emphasis on the use of rigorous techniques as an aid to understanding and building modern computing systems. Particular attention is paid to concurrent and distributed systems. Topics covered include: specification and verification, concurrent algorithms, synchronization, naming, networking, replication techniques (including distributed cache management), and principles and algorithms for achieving reliability.
Textbook: http://ocw.mit.edu/courses/electricalengineeringandcomputerscience/6826principlesofcomputersystemsspring2002/lecturenotes/
EECS 457 Performance Engineering of Software Systems (3+0)3  ECTS=5
Description: Modern computing platforms provide unprecedented amounts of raw computational power. But significant complexity comes along with this power, to the point that making useful computations exploit even a fraction of the potential of the computing platform is a substantial challenge. Indeed, obtaining good performance requires a comprehensive understanding of all layers of the underlying platform, deep insight into the computation at hand, and the ingenuity and creativity required to obtain an effective mapping of the computation onto the machine. The reward for mastering these sophisticated and challenging topics is the ability to make computations that can process large amount of data orders of magnitude more quickly and efficiently and to obtain results that are unavailable with standard practice. This course is a handson, projectbased introduction to building scalable and highperformance software systems. Topics include: performance analysis, algorithmic techniques for high performance, instructionlevel optimizations, cache and memory hierarchy optimization, parallel programming, and building scalable distributed systems.
Textbook: http://ocw.mit.edu/courses/electricalengineeringandcomputerscience/6172performanceengineeringofsoftwaresystemsfall2009/lecturenotes/
EECS 461 Machine Learning (3+0)3  ECTS=5
Description: This course is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The main theme in the course is statistical inference that provides the foundation for most of the methods covered.
Textbook: Pattern Classification, 2nd edition, by Duda, Richard, Peter Hart, and David Stork. WileyInterscience, 2000.
EECS 464 Computer Vision (3+0)3  ECTS=5
Description: Application of computer vision techniques to consumerlevel applications such as stitching, exposure bracketing, morphing, etc. will be the focus. Topics covered will include an overview of problems in computer vision, image formation, image processing, feature detection and matching, segmentation, dense motion estimation, and stereo correspondence as time permits.
Textbook: Computer Vision: Algorithms and Applications, by Richard Szeliski. Springer, 2010.
EECS 465 Introduction to Robotics (3+0)3  ECTS=5
Description: This is a course on modeling, design, planning and control of robot systems. It surveys results from geometry, kinematics, statics, dynamics and control theory.
Textbook: Introduction to Robotics: Mechanics and control, John J. Craig. Prentice Hall, 2005.
EECS 468 Bioinformatics (3+0)3  ECTS=5
Description: This course is an algorithmic principles driving in bioinformatics. It emphasizes the relatively few design techniques used in diverse range of practical problems in biology such as DNA mapping, genome rearrangements, statistical methods for gene prediction and molecular evolution.
Textbook: An introduction to Bioinformatics, Jones and Pevzner. MIT Press, 2005.
EECS 471 Distributed Algorithms (3+0)3  ECTS=5
Description: This course provides an introduction to the most important basic results in the area of distributed algorithms, and prepare interested students to begin independent research or take a more advanced course in distributed algorithms. Distributed algorithms are algorithms designed to run on multiple processors, without tight centralized control. In general, they are harder to design and harder to understand than singleprocessor sequential algorithms. Distributed algorithms are used in many practical systems, ranging from large computer networks to multiprocessor sharedmemory systems. They also have a rich theory, which forms the subject matter for this course. The core of the material will consist of basic distributed algorithms and impossibility results. This will be supplemented by some updated material on topics such as selfstabilization, waitfree computability, and failure detectors, and some new material on scalable sharedmemory concurrent programming.
Textbook: Distributed Algorithms, by Lynch, Nancy. Morgan Kaufmann, 1996.
EECS 474 Networks (3+0)3  ECTS=5
Description: Networks are ubiquitous. Internet that links us to and enables information flows with the rest of the world is the most visible example. Our society is organized around networks of friends and colleagues. These networks determine our information, influence our opinions, and shape our political attitudes, and link us to everybody else in Turkey and in the world. Economic and financial markets look much more like networks than anonymous marketplaces. Firms interact with the same suppliers and customers and use Weblike supply chains. Financial linkages, both among banks and between consumers, companies and banks, also form a network over which funds flow and risks are shared. Systemic risk in financial markets often results from the counterparty risks created within this financial network. Food chains, interacting biological systems and the spread and containment of epidemics are some of the other natural and social phenomena that exhibit a marked networked structure. This course will introduce the tools for the study of networks. It will show how certain common principles permeate the functioning of these diverse networks and how the same issues related to robustness, fragility, and interlinkages arise in different types of networks.
Textbook:
1. Networks, Crowds, and Markets: Reasoning about a Highly Connected World, by Easley, David, and Jon Kleinberg.Cambridge University Press, 2010.
2. Social and Economic Networks, by Jackson, Matthew O. Princeton University Press, 2008.
EECS 481 Introduction to Cryptography (3+0)3  ECTS=5
Description: This is an introductory course on methods, algorithms, techniques, and tools of cryptography. We study in detail algorithmic and mathematical aspects of cryptographic methods and protocols, such as secretkey cryptography, publickey cryptography, hash functions, and digital signatures. We show how these techniques are used to solve particular data and communication security problems. This course material is useful for computer science, electrical engineering, and mathematics students who are interested in learning how cryptographic algorithms and methods are embedded in information systems, providing confidentiality, integrity, nonrepudiation, and authenticity of stored and transmitted digital data.
Textbook:
1. Introduction to Cryptography, by J. A. Buchmann. Springer, 2004.
2.2Computer Security and Cryptography, by A. G. Konheim. Wiley, 2007.
EECS 482 Cryptographic Engineering (3+0)3  ECTS=5
Description: This is a graduate course is designed for computer science, mathematics, electrical engineering students interested in understanding, designing, developing, testing, and validating cryptographic software and hardware. We will study algorithms, methods, and techniques in order to create stateofart cryptographic embedded software and hardware using common platforms and technologies.
Textbook: Cryptographic Engineering, by C. K. Koc. Springer, 2009.
EECS 483 Network Security (3+0)3  ECTS=5
Description: In this course, we study the theoretical and practical aspects of network security. We start with a threat model, and describe vulnerabilities of computer networks to attacks by adversaries and hackers using a variety of techniques. We then study methods and techniques to circumvent or defend against these attacks and to minimize their damage. In this context, we study cryptographic techniques and protocols, network security protocols, digital signatures and authentication protocols, network security practice, and wireless network security. Security attacks, mechanisms, and services. Network security and access security models. Overview of secretkey and publickey cryptography. Authentication protocols and key management. Network security practice. Email security. IP security and web security. Intrusion detection and prevention systems. Firewalls and virtual private networks. Wireless network security.
Textbook: Network Security, 2nd Edition, C. Kaufman, R. Perlman, M. Speciner. Prentice Hall, 2002.
FRESHMAN YEAR 

I.
SEMESTER 

COURSE
CODE 
COURSE
NAME 
T 
P 
CR 
ECTS 
PREREQ. 
UNI 111 
3 
0 
3 
5 
NONE 

UNI xxx 
Core Course Elective I 
3 
0 
3 
5 
NONE 
LIFE 101 
3 
0 
3 
5 
NONE 

MATH 103 
3 
2 
4 
6 
NONE 

PHYS 103 
3 
2 
4 
6 
NONE 

PHYS 103L 
0 
2 
1 
1 
NONE 

ENGR 105 
Noncredit 
2 
NONE 

SEMESTER
TOTAL 
18 
30 


II.
SEMESTER 

COURSE
CODE 
COURSE
NAME 
T 
P 
CR 
ECTS 
PREREQ. 
UNI 112 
3 
0 
3 
5 


UNI xxx 
Core Course Elective II 
3 
0 
3 
5 

LIFE 102 
3 
0 
3 
5 


MATH 104 
3 
2 
4 
6 
MATH 103 

PHYS 104 
3 
2 
4 
6 


PHYS 104L 
0 
2 
1 
1 


ENGR 100 
Computer Skills 
Noncredit 
2 


SEMESTER
TOTAL 
18 
30 


UNI 100 
3 
0 
3 
5 

_
SOPHOMORE YEAR 

III.
SEMESTER 

COURSE
CODE 
COURSE
NAME 
T 
P 
CR 
ECTS 
PREREQ. 
ENGR 211 
2 
2 
3 
5 
ENGR 105, ENGR 100 

MATH 205 
3 
0 
3 
5 
MATH 103 

EECS 201 
3 
2 
4 
6 
ENGR 105, ENGR 100 

EECS 241 
Discrete Mathematics 
3 
0 
3 
4 
MATH 103 
UNI xxx 
Core Course Elective III 
3 
0 
3 
5 

UNI xxx 
Core Course Elective IV 
3 
0 
3 
5 

SEMESTER
TOTAL 
19 
30 


IV.
SEMESTER 

COURSE
CODE 
COURSE
NAME 
T 
P 
CR 
ECTS 
PREREQ. 
ENGR 212 
2 
2 
3 
5 
ENGR 211 

ENGR 251 
2 
2 
3 
5 
ENGR 105, MATH 104 

EECS 202 
3 
2 
4 
6 
ENGR 105, ENGR 100 

EECS 218 
Digital Logic Design 
3 
0 
3 
5 
EECS 241 
CS 240 
Exploratory Data Analysis 
3 
0 
3 
4 
ENGR 105, ENGR 100 
UNI xxx 
Core Course Elective V 
3 
0 
3 
5 

SEMESTER
TOTAL 
19 
30 


CS 200 
Summer Practice (25 work days) 
Noncredit 
5 

JUNIOR YEAR 

V. SEMESTER 

COURSE CODE 
COURSE NAME 
T 
P 
CR 
ECTS 
PREREQ. 
UNI 123 
3 
0 
3 
5 


CS 351 
Computer Architecture 
3 
0 
3 
5 
EECS 218 
CS 361 
3 
0 
3 
5 
ENGR 212 

CS 371 
3 
0 
3 
5 
ENGR 211 

xxx 
General Elective I 
3 
0 
3 
5 

UNI 201 
3 
0 
3 
5 


SEMESTER TOTAL 
18 
30 


VI. SEMESTER 

COURSE CODE 
COURSE NAME 
T 
P 
CR 
ECTS 
PREREQ. 
UNI 124 
3 
0 
3 
5 


CS 352 
3 
0 
3 
5 
ENGR 212 

CS 362 
Machine Intelligence 
3 
0 
3 
5 
ENGR 212 
CS 372 
3 
0 
3 
5 
CS 371 

CS 340 
Computer Systems 
3 
0 
3 
5 
CS 361 
UNI 202 
3 
0 
3 
5 


SEMESTER TOTAL 
18 
30 


CS 300 
Summer Practice
(25 work days) 
Noncredit 
5 

SENIOR YEAR 

VII. SEMESTER 

COURSE CODE 
COURSE NAME 
T 
P 
CR 
ECTS 
PREREQ. 
ENGR 497 
1 
2 
2 
10 
Senior Standing 

EECS xxx 
Departmental Elective
I 
3 
0 
3 
5 
Senior Standing 
EECS xxx 
Departmental Elective
II 
3 
0 
3 
5 
Senior Standing 
EECS xxx 
Departmental Elective
III 
3 
0 
3 
5 
Senior Standing 
xxx 
General Elective II 
3 
0 
3 
5 

SEMESTER TOTAL 
14 
30 


VIII. SEMESTER 

COURSE CODE 
COURSE NAME 
T 
P 
CR 
ECTS 
PREREQ. 
ENGR 498 
1 
2 
2 
10 
ENGR 497 

EECS xxx 
Departmental Elective
IV 
3 
0 
3 
5 
Senior Standing 
EECS xxx 
Departmental Elective
V 
3 
0 
3 
5 
Senior Standing 
EECS xxx 
Departmental Elective
VI 
3 
0 
3 
5 
Senior Standing 
xxx 
General Elective III 
3 
0 
3 
5 

SEMESTER TOTAL 
14 
30 

Abbreviations: T (Theory),
P (Practice), Cr (Credit),
ECTS credit
Total Credits Required for Graduation: 141
Total Credits of Electives:
42
No. of Courses: 49
Average Credit Load Per Semester: 17.6
Elective Ratio: 30%
6 Departmental Electives from
the tracks defined below with the approval of the academic advisor. (At least 3 must be from the same track.)
3 General Electives are taken from other
colleges or schools.
5 Core Course (Humanities) Electives are taken
from the pool of Core Course Curriculum listed below.
EECS TRACKS and DEPARTMENTAL ELECTIVES Ð 3 credits each (=5 ECTS credits) 

All EECS 4** elective courses have the prerequisite of "senior standing". Second and third
year students can not take them. 

COMPUTER
SYSTEMS 

COURSE
CODE 
COURSE
NAME 
EECS 451 
Database Systems 
EECS 456 
Principles of Computer Systems 
EECS 457 
Performance Engineering of Software Systems 
EECS 464 
Computer Vision 
EECS 482 
Cryptographic Engineering 
EECS 483 
Network
Security 
EECS 420 
Software Based Startups 
EECS 428 
Data Visualization 
EECS 429 
Multimedia
Systems 
EECS 432 
Mobile
Programming 
EECS 475 
Computer Networks 
DEVICES 

COURSE
CODE 
COURSE
NAME 
EECS 403 
Digital Circuit Design 
EECS 404 

EECS 405 
RF Circuit Design 
EECS 407 
Advanced Integrated Analog Circuit
Design 
EECS 422 
Wave Propagation and Antennas 
EECS 423 
Optics 
EECS 424 
Photonics and Lasers 
EECS 441 
Nano and Micro Electro Mechanical
Systems 
EE
SYSTEMS 

COURSE
CODE 
COURSE
NAME 
EECS 413 

EECS 415 

EECS 416 
Image and
Video Processing 
EECS 465 
Introduction to Robotics 
THEORY
and ALGORITHMS 

COURSE
CODE 
COURSE
NAME 
EECS 418 
Information
Theory 
EECS 461 
Machine
Learning 
EECS 462 
Automata Theory, Languages
and Computation 
EECS 465 
Introduction to Robotics 
EECS 468 

EECS 471 
Distributed
Algorithms 
EECS 474 
Networks 
EECS 481 
Cryptography 
EECS 485 
Cognitive Computing 
EECS 486 
Computational Genomics 
OTHER DEPARTMENTAL or COLLEGE
ELECTIVES 

COURSE
CODE 
COURSE
NAME 
ENGR 422 
Numerical Methods 
EECS 402 
Entrepreneurship 
Other college / departmental elective courses, including courses from the graduate programs, may be taken with the consent of the academic advisor.
THE POOL of CORE COURSES CURRICULUM  3 credits
each (=5 ECTS credits) 

UNI 221 World Civilizations and Global Encounters: Until 1300 CE 
UNI 222 World Civilizations and Global Encounters: Since 1300 CE 
Other UNI coded core courses may
be taken with the consent of the academic advisor and approval of the College Administrative Board.
This new curriculum
will be implemented for the
new freshman students who entered
the College in the year
2014.
For the existing sophomore, junior and senior students, the College Board will make plans for proper adaptation to the new curriculum.
Students who have
not taken or who have failed
EECS 216 will now take CS 340 instead.
Students who have
not taken or who have failed
MATH 206 will now take CS 240 instead.
Students who have
taken CS 100 will be exempted from taking
ENGR 100. If anyone has failed CS 100, s/he will take ENGR 100 instead.
The prerequisities will become effective,
for all new and existing students, from Spring 2016.
College Administration Board decision
is required to by pass a prerequisite
for any course.
Senior standing means the student has successfully completed at least 100 credit units in the program.
Junior standing means the student has successfully completed at least 65 credit units in the program.
Computer Science Courses as a Second Major  

Students from other engineering Programs  
COURSE CODE  COURSE NAME  T  P  CR 
ENGR 211  Introduction to Programming  3  
ENGR 212  Programming Practice  3  
MATH 205  Linear Algebra and Differential Equations  3  
ENGR 251  Probability for Engineers  3  
EECS 201  EECS I: System Fundamentals  4  
EECS 202  EECS II: Network Analysis  4  
EECS 216  Signals and Systems  3  
CS 351  Computer Architecture  3  
CS 352  Computer Systems  3  
CS 361  Software  3  
CS 362  Machine Intelligence  3  
CS 371  Data Structures and Algorithms  3  
ENGR 497  Global Design Project I  2  
ENGR 498  Global Design Project II  2  
EECS 4xx  Departmental Elective I  3  
EECS 4xx  Departmental Elective II  3  
Total  38  
Students from other engineering Programs  
COURSE CODE  COURSE NAME  T  P  CR 
ENGR 211  Introduction to Programming  3  
ENGR 212  Programming Practice  3  
MATH 205  Linear Algebra and Differential Equations  3  
ENGR 251  Probability for Engineers  3  
EECS 201  EECS I: System Fundamentals  4  
EECS 202  EECS II: Network Analysis  4  
EECS 216  Signals and Systems  3  
CS 351  Computer Architecture  3  
CS 352  Computer Systems  3  
CS 361  Software  3  
CS 362  Machine Intelligence  3  
CS 371  Data Structures and Algorithms  3  
ENGR 497  Global Design Project I  2  
ENGR 498  Global Design Project II  2  
EECS 4xx  Departmental Elective I  3  
EECS 4xx  Departmental Elective II  3  
LIFE 101  Life Sciences I  Chemistry  3  
LIFE 102  Life Sciences II  Biology  3  
MATH 103  Calculus I  Differential  4  
MATH 104  Calculus II  Integral  4  
PHYS 103  Physics I  Mechanics and Dynamics  4  
PHYS 103L  Physics I Lab  1  
PHYS 104  Physics II  Electromagnetics and Modern Physics  4  
PHYS 104L  Physics II Lab  1 
Abbreviations:
T (Theory), P (Practice), Cr (Credit)
*Courses other than those listed can be accepted with the approval of the advisor.
**Any student who has already taken any of those courses listed in Group A and Group B as part of another major IS NOT required to take courses as replacement except those listed as EECS 4xx Departmental Electives.
Computer Science Courses as a Minor Area  

Engineering Students  
COURSE CODE  COURSE NAME  T  P  CR 
ENGR 211  Introduction to Programming  3  
ENGR 212  Programming Practice  3  
EECS 201  EECS I: System Fundamentals  4  
EECS 202  EECS II: Network Analysis  4  
+ 2 Courses from EExx  
+ 2 Courses from EECSxx 
Abbreviations:
T (Theory), P (Practice), Cr (Credit)
* Plus two (2) courses from the list of EE 3xx and two (2) courses from the list of EECS 4xx codes. Any courses with EE3xx and EECS4xx code as part of the major cannot be used to satisfy minor requirement
Educational Mission of the Computer Science and Engineering Program
Computer Science and Engineering undergraduate program at İstanbul Şehir University prepares its graduates to become intellectual leaders in industry, government, and academia.
Graduates of this program are grounded in scientific, mathematical, and technical knowledge through coursework that keeps pace with current relevant technologies. They develop the ability to analyze, synthesize, and design both core parts of modern computing systems and integrated application systems centered at computers. They learn the scientific foundation for hardware and software engineering and apply it in engineering exercises.
By means of general education courses, they enhance their ability to communicate and acquire an understanding and appreciation for other areas of human intellectual achievement.
Educational Objectives of Computer Science and Engineering Program
Graduates of the Computer Science and Engineering undergraduate program will:
Note: Program educational objectives are those aspects of engineering that help shape the curriculum; achievement of these objectives is a shared responsibility between the student and İstanbul Şehir University.
Program Outcomes of Computer Science and Engineering
Graduates of the Computer Science and Engineering undergraduate program will have: