Programs

Graduate Programs


MS in Electronics and Computer Engineering

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 co-design. Our graduates will have in-depth 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 one-year (2 academic-year terms and one summer term), and all accepted students will be offered scholarships.

ADMISSION REQUIREMENTS

Graduate Programs in the GS-NAS 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:

  • Analysis, differential equations and linear algebra
  • Computer programming in one of C++, FORTRAN or Java
  • Probability and Statistics
  • Operations Research


ADMISSION CRITERIA

Admission to the MS programs depend on

  • Cumulative GPA,
  • Cumulative GPA,
  • Good command of English as measured by TOEFL IELTS or similar exams,
  • Letters of recommendation
  • Letter of interest
  • Interview by the admission committee


APPLICATION – REGISTRATION CALENDAR

Application to Graduate Programs can be made year-around 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:

  • Around June 10 for the fall semester, and
  • Around December 15 for the spring semester.

Application Deadlines for programs without thesis option:

  • Around August 1 for the fall semester, and
  • Around January 1 for the spring semester.


ONLINE APPLICATION

  • All applications should be made online at www.sehir.edu.tr/graduate *
  • All necessary documents can be uploaded in the following formats: Word, PDF, JPEG, GIF.
  • Uploaded documents and information are saved in the system permanently and cannot be deleted, changed or corrected once online application has been completed.
  • Following documents must be uploaded at the online application system:
  • Transcript(s) (scanned / digital copies)
  • Statement of Purpose (PDF or Word; see FAQ for the content of SOP)
  • Detailed CV (PDF, Word)
  • GRE or ALES Scores (scanned / digital copies)
  • English Proficiency Scores (TOEFL, IELTS, ÜDS, KPDS) (scanned / digital copies)
  • Online application cannot be completed until all required fields have been filled.
  • At least two letters of recommendation should be sent to Graduate Admission Committee via e-mail to fbe@sehir.edu.tr.
  • If any, academic paper samples can be also sent to fbe@sehir.edu.tr.
  • Originals of all documents must be submitted during registration.
  • If you are applying for more than one program, you should fill a separate online form for each program.
  • For application form, please click here. *

*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)

  • An official copy of transcript(s) (in a sealed envelope),
  • Diploma/interim certificate of graduation,
  • Updated CV (resumé)
  • GRE or ALES Scores,
  • English Proficiency Scores (TOEFL, IELTS, ÜDS, KPDS),
  • A copy of the passport
  • Two passport photos
  • A photocopy of your student visa obtained from the Turkish Embassy or Consulate in your country (for international students)

SCHOLARSHIPS and FINANCIAL AID

  • Limited but competitive scholarships are offered after a merit-base assessment of all applications.
  • 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 scholarship.
  • Scholarship levels may change from a tuition-waver plus health insurance level to 1600TL/month stipend plus tuition-waver, free lunch, health insurance, research support, accommodation support, etc., which may total up to 2000TL/month.
  • Students with scholarships may be required for teaching assistantship duties up to 20 hours/week.
  • Students may also obtain additional support from their advisers if they involve in sponsored research projects.
  • Financial Aid is only in the form of tuition-waver, and determined after a merit-base assessment of applications.

İstanbul Şehir University Placement Exam and STEP-Sehir 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
  • 2. Proficiency Exam (STEP-Sehir Test of English Proficiency)
    • STEP-Written Exam (Reading, listening, writing)
    • STEP-Oral Exam (Speaking)

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 STEP-English 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 placement exam will be given on Wednesday, September 3rd, 2014 at 10:00 at İstanbul Şehir University Altunizade South Campus. The duration of the exam is 50 minutes. The results will be announced the same day at 1 pm.
  • As a result of the placement exam, all candidates at and over intermediate level gain the right to take the STEP-English Proficiency Exam. It is an exam measuring the general and academic English proficiency of those who want admission for the graduate programs.

The exam will be assessed according to the pre-determined 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 7-10 questions on text 2 and 12-20 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 8-10 questions while listening to a conversation. The second part is worth 15% and takes 25 minutes in total to complete. There are 8-15 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 15-20 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, 7-10 questions, 70 minutes with Text 3
Text 3: 20%, approx. 2000 words, 12-20 questions, 70 minutes with Text 2
Total Time: 80 minutes
Listening While Listening: 10 %, approx. 10 minutes, 7-10 questions
Listening Note-taking: 15 %, approx. 12-16 minutes listening and 10 minutes to answer questions, 8-15 questions.
Total Time: 35-40 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. 15-25 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 STEP-English 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 ​unrefund​able.

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
LS-DYNA 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 small-scale 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 (P-N) 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:

  • Complete core courses (3 courses listed below).
  • Complete five (5) technical elective courses as recommended by their adviser.
  • Register for the graduate seminar courses ECE 591A and ECE591B, compulsory and non-credit. Students should present a seminar at least once (either in ECE591A or ECE591B) and attend at least 80% of the seminars.
  • Prepare, submit and successfully defend thesis in ECE 599 under the supervision of an adviser.



OPTION WITHOUT THESIS

The following program requirements must be completed to earn a MS degree in ECE with Computer Science Track without thesis:

  • Complete core courses (3 courses as listed below).
  • Complete seven (7) technical elective courses as recommended by their advisor
  • Register for the graduate seminar courses ECE 591A and ECE 591B, compulsory and non-credit. Students should attend at least 80% of the seminars.
  • Prepare, submit and successfully defend a project in ECE 595 course under the supervision of an adviser.


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:

  • Complete at least three core courses out of six core courses for Track 2. Completing all six core courses is recommended.
  • Take EE 413 Wireless Communications and EE415 Digital Signal Processing if not taken in undergraduate.
  • Complete five technical elective courses as recommended by their adviser. ECE 516, ECE 517, ECE 529 and ECE 590 are the other recommended courses for this track. EE 413 and EE 415 are counted as technical electives.
  • Register for the graduate seminar courses ECE 591A and ECE591B, compulsory and non-credit. Students should present a seminar at least once (either in ECE591A or ECE591B) and attend at least 80% of the seminars.
  • Prepare, submit and successfully defend thesis in ECE 599 under the supervision of an adviser.



OPTION WITH THESIS

The following program requirements must be completed to earn a MS degree in ECE with Communication and Signal Processing Track:

  • Complete at least three core courses out of six core courses for track 2. Completing all six core courses is recommended.
  • Take EE 413 Wireless Communications and EE415 Digital Signal Processing if not taken in undergraduate.
  • Complete seven technical elective courses as recommended by their adviser. ECE 516, ECE 517, ECE 529 and ECE 590 are the other recommended courses for this track. EE 413 and EE 415 are counted as technical electives.
  • Register for the graduate seminar courses ECE591A and ECE591B, compulsory and non-credit. Students should attend at least 80% of the seminars.
  • Prepare, submit and successfully defend a project in ECE 595 course under the supervision of an adviser.


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:

  • Complete at least three core courses out of six core courses for track 3. Completing all six core courses is recommended.
  • Take EE 413 Wireless Communications and EE415 Digital Signal Processing if not taken in undergraduate.
  • Complete five technical elective courses as recommended by their adviser. ECE 515, ECE 509, ECE 522 and ECE 590 are the other recommended courses for this track. EE 413 and EE 415 are counted as technical electives.
  • Register for the graduate seminar courses ECE 591A and ECE591B, compulsory and non-credit. Students should present a seminar at least once (either in ECE591A or ECE591B) and attend at least 80% of the seminars.
  • Prepare, submit and successfully defend thesis in ECE 599 under the supervision of an adviser.



OPTION WITH THESIS

The following program requirements must be completed to earn a MS degree in ECE with System and Circuit Design Track:

  • Complete at least three core courses out of six core courses for track 3. Completing all six core courses is recommended.
  • Take EE 413 Wireless Communications and EE415 Digital Signal Processing if not taken in undergraduate.
  • Complete five technical elective courses as recommended by their adviser. ECE 515, ECE 509, ECE 522 and ECE 590 are the other recommended courses for this track. EE 413 and EE 415 are counted as technical electives.
  • Register for the graduate seminar courses ECE591A and ECE591B, compulsory and non-credit. Students should attend at least 80% of the seminars.
  • Prepare, submit and successfully defend a project in ECE 595 course under the supervision of an adviser.


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 over-sampling digital-to-analog and analog-to-digital 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.

Pre-requisites: A course on electronic circuits (MOS Transistors) and discrete-time 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 mm-Wave 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, delay-locked loops (DLLs), fractional-N 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 least-squares 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 rate-distortion 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, time-varying 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 three-dimensional 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, spatio-temporal filtering, camera calibration and hand-eye coordination, robot navigation, shape representation, physically-based modeling, regularization theory, multi-sensory 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 top-down and bottom-up 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, NP-complete problems, polynomial transformations, Turing reductions, strong NP-completeness, NP-hardness 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 man-made networks such as the Internet, communication networks, peer-to-peer networks, sensor networks, networks-on-a 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 graph-theoretical approaches.

ECE 563 Combinatorial Algorithms

Many large-scale 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 state-of-the-art and state-of-the-practice activities in the knowledge and data engineering area. We are interested in well-defined 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) real-time 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) knowledge-based 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 fault-tolerance. Topics include event ordering, clocks, global states, agreement, fault-tolerance, and peer-to-peer 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, out-of-order execution, memory-hierarchy 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 hands-on, learn-by-doing 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 front-end 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 under-exploited 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 human-computer 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 (programming-by-demonstration, visualization), and human-centered 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 secret-key and public-key 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 "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. The class provides examples of real-world 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 data-analytically, and fully appreciate how data science methods can support business decision-making.

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 secret-key cryptography, public-key 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 state-of-art 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.

MS in Data Science

From the rise of Internet to this day, the Web has evolved from being a one way communication environment to many-to-many 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 in-transit 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 4­-step 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 “what­-if” analysis, data scientists require powerful back-­end 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 up-front 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 merit-base assessment of all applications.

Scholarship levels may change from a partial tuition-waver 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 state-of-the-art and state-of-the-practice activities in the knowledge and data engineering area. We are interested in well-defined 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 text-based 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 "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. The class provides examples of real-world 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 data-analytically, and fully appreciate how data science methods can support business decision-making.

ECE 578 Data Visualization

The increasing scale and accessibility of digital data – including government records, corporate databases, and logs of online activity – provides an under-exploited 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 human-computer 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 (programming-by-demonstration, visualization), and human-centered 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 hands-on, learn-by-doing 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 front-end 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 man-made networks such as the Internet, communication networks, peer-to-peer networks, sensor networks, networks-on-a chip, power grid, etc. and social networks such as acquaintance networks, organizational networks, online communities etc. The main analytical tools used are graph-theoretical 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 two-part 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 Neyman-Pearson hypothesis testing; Bayesian and nonrandom parameter estimation; minimum-variance unbiased estimators and the Cramer-Rao bounds; representations for stochastic processes, shaping and whitening filters, and Karhunen-Loeve 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, state-space, and input-output models; model structures, parametrization, and identifiability; non-parametric 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 on-demand 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:

  • Must complete at least 24 credits of course work (8 courses), in addition to a seminar course.
  • Must complete a research thesis under the supervision of a faculty adviser.


MS without Thesis:

  • Must complete at least 30 credits of course work (10 courses), in addition to a seminar course.
  • Must complete and present a term project under the supervision of an adviser.


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

  • Students who do not receive scholarship of other institutions have priority at our University’s scholarship program. In cases when the student receives scholarship from another institution, the status of that scholarship is revised. Students who work at another workplace cannot benefit from other scholarship opportunities than tuition waiver.
  • Tuition waiver is applicable for 3 years in Graduate Programs and for 6 years in Post-Graduate Programs. If this duration is prolonged, the course/thesis/project registration fee determined by the University Board of Directors is collected.
  • Student is required to take minimum 2 courses each semester and have a GPA of 3.00 at the end of the year. If the GPA of student is below 2.50 at the end of any semester, the scholarship is cut-off.
  • At the end of each 7 weeks, the academic staff with whom the student works or the thesis advisor prepares a timetable where the student is evaluated.
  • At the end of each semester, student is evaluated according the performance drawn in the time table and the academic standing. Scholarship of a student can be increase if he/she has a high level of performance and academic standing for two semesters.
  • The payments made to a student within the context of the scholarship (in kind/ in cash) are cut-off for the next semester if the student demonstrates an insufficient performance and/or academic failure for a semester without a valid excuse. If this situation continues for a year, the tuition waiver is repealed.

Undergraduate Programs


BS in Computer Science and Engineering

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. Addison-Wesley.



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. Addison-Wesley.



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 (Non-credit) – 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 sub-disciplines.



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 hands-on 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 in-class 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 hands-on 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 non-binary systems, Boolean algebra, digital design techniques, logic gates, logic minimization, standard combinational circuits, sequential circuits, flip-flops, 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. MacGraw-Hill, 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 discrete-time processing of continuous-time signals, modulation, Laplace and Z-transforms, and feedback systems.

Textbook: Signals and Systems, 2nd edition, by Oppenheim, A. V., and A. S. Willsky, with H. Nawab. Prentice-Hall, 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, instruction-set architecture, memory hierarchy, and communication primitives. Using a field-programmable 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 in-class 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 client-server design, operating systems; performance, networks; naming; security and privacy; fault-tolerant 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 Object-Oriented Design, by Liskov, Barbara, and John Guttag. Addison-Wesley, 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 intelligent-system 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. Addison-Wellsley, 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; divide-and-conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow; computational geometry; number-theoretic 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 product-centric 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 A-to-Z 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, solid-state memory, large-scale integrated circuits, and design of electronic systems. Parasitics, transmission-line 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, delay-locked loops (DLLs), fractional-N 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 multi-carrier wireless modulation schemes. Diversity techniques. Multiple-access 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. Fast-Fourier 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 spatio-temporal sampling, motion analysis, parametric motion models, motion-compensated filtering, and video processing operations including noise reduction, restoration, superresolution, deinterlacing and video sampling structure conversion, and compression (frame-based and object-based methods).

Textbook:
1. "Video Processing and Communications" by Yao Wang, Joern Ostermann, and Ya-Qin Zhang, Prentice Hall, 2002, ISBN 0-13-017547-1.
2. "Fundamentals of Digital Image Processing" by A.K.Jain, Prentice-Hall, 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; pub-sub 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. McGraw-Hill, 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/electrical-engineering-and-computer-science/6-826-principles-of-computer-systems-spring-2002/lecture-notes/



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 hands-on, project-based introduction to building scalable and high-performance software systems. Topics include: performance analysis, algorithmic techniques for high performance, instruction-level optimizations, cache and memory hierarchy optimization, parallel programming, and building scalable distributed systems.

Textbook: http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-172-performance-engineering-of-software-systems-fall-2009/lecture-notes/



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. Wiley-Interscience, 2000.



EECS 464 Computer Vision (3+0)3 - ECTS=5

Description: Application of computer vision techniques to consumer-level 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 single-processor sequential algorithms. Distributed algorithms are used in many practical systems, ranging from large computer networks to multiprocessor shared-memory 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 self-stabilization, wait-free computability, and failure detectors, and some new material on scalable shared-memory 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 Web-like 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 secret-key cryptography, public-key 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, non-repudiation, 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 state-of-art 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 secret-key and public-key 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

PRE-REQ.

UNI 111

Critical Reading and Writing in Turkish I

3

0

3

5

NONE

UNI xxx

Core Course Elective I

3

0

3

5

NONE

LIFE 101

Life Sciences I - Chemistry

3

0

3

5

NONE

MATH 103

Calculus I - Differential

3

2

4

6

NONE

PHYS 103

Physics I - Mechanics and Dynamics

3

2

4

6

NONE

PHYS 103L

Physics I - Lab

0

2

1

1

NONE

ENGR 105

Introduction to  Engineering

Non-credit

2

NONE

SEMESTER TOTAL

18

30

 

II. SEMESTER

COURSE CODE

COURSE NAME

T

P

CR

ECTS

PRE-REQ.

UNI 112

Critical Reading and Writing in Turkish II

3

0

3

5

 

UNI xxx

Core Course Elective II

3

0

3

5

 

LIFE 102

Life Sciences II - Biology

3

0

3

5

 

MATH 104

Calculus II - Integral

3

2

4

6

MATH 103

PHYS 104

Physics II - Electromagnetics & Modern Physics

3

2

4

6

 

PHYS 104L

Physics II - Lab

0

2

1

1

 

ENGR 100

Computer Skills

Non-credit

2

 

SEMESTER TOTAL

18

30

 

UNI 100

Exploring  Istanbul

3

0

3

5

 

_

SOPHOMORE YEAR

III. SEMESTER

COURSE CODE

COURSE NAME

T

P

CR

ECTS

PRE-REQ.

ENGR 211

Introduction to Programming

2

2

3

5

ENGR 105, ENGR 100

MATH 205

Linear Algebra

3

0

3

5

MATH 103

EECS 201

System Design Fundamentals

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

PRE-REQ.

ENGR 212

Programming Practice

2

2

3

5

ENGR 211

ENGR 251

Probability for Engineers

2

2

3

5

ENGR 105, MATH 104

EECS 202

Basic Digital Communication with Networking

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)

Non-credit

5

 

 

JUNIOR YEAR

V. SEMESTER

COURSE CODE

COURSE NAME

T

P

CR

ECTS

PRE-REQ.

UNI 123

Textual Analysis and Effective Communication

3

0

3

5

 

CS 351

Computer Architecture

3

0

3

5

EECS 218

CS 361

Software

3

0

3

5

ENGR 212

CS 371

Data Structures and Algorithms

3

0

3

5

ENGR 211

xxx

General Elective I

3

0

3

5

 

UNI 201

Formations of Modern Turkey I

3

0

3

5

 

SEMESTER TOTAL

18

30

 

VI. SEMESTER

COURSE CODE

COURSE NAME

T

P

CR

ECTS

PRE-REQ.

UNI 124

Textual Analysis and Academic Writing

3

0

3

5

 

CS 352

Operating Systems

3

0

3

5

ENGR 212

CS 362

Machine Intelligence

3

0

3

5

ENGR 212

CS 372

Advanced Algorithms

3

0

3

5

CS 371

CS 340

Computer Systems

3

0

3

5

CS 361

UNI 202

Formations of Modern Turkey II

3

0

3

5

 

SEMESTER TOTAL

18

30

 

CS 300

Summer Practice (25 work days)

Non-credit

5

 

 

SENIOR YEAR

VII. SEMESTER

COURSE CODE

COURSE NAME

T

P

CR

ECTS

PRE-REQ.

ENGR 497

Global Design Project I

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

PRE-REQ.

ENGR 498

Global Design Project II

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 pre-requisite 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

Communication Circuits

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

Wireless Communications

EECS 415

Digital Signal Processing

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

Bioinformatics

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 117 Understanding Society and Culture

UNI 118 Understanding Politics and Economy

UNI 221 World Civilizations and Global Encounters: Until 1300 CE

UNI 222 World Civilizations and Global Encounters: Since 1300 CE

UNI 102 Critical Thinking

UNI 205 Understanding Science and Environment

UNI 209 Understanding Cultural Encounters

UNI 211 Understanding Art and Architectural Encounters

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 pre-requisite 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:

  1. Engage in professional practice in academia, industry, or government;
  2. Promote innovation in the design, research and implementation of products and services in the field of Computer Science and Engineering through strong communication, leadership and entrepreneurial skills;
  3. Engage in life-long learning in the field of Computer Science and Engineering.

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:

  • An ability to apply knowledge of mathematics, science, and engineering
  • An ability to design and conduct experiments, as well as to analyze and interpret data
  • An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
  • An ability to function on multidisciplinary teams
  • An ability to identify, formulate, and solve engineering problems
  • An understanding of professional and ethical responsibility
  • An ability to communicate effectively
  • The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context
  • A recognition of the need for, and an ability to engage in life-long learning
  • A knowledge of contemporary issues
  • An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice.
İstanbul Şehir University
Altunizade Mah. Oymacı Sok. No: 15
34660 Istanbul, Turkey
Email: cs@sehir.edu.tr
Phone: +90 216 559 9000