Özyeğin University, Çekmeköy Campus Nişantepe District, Orman Street, 34794 Çekmeköy - İSTANBUL

Phone : +90 (216) 564 90 00

Fax : +90 (216) 564 99 99

E-mail: info@ozyegin.edu.tr

M.Sc. in Artificial Intelligence

M.Sc. in Artificial Intelligence


In recent years, we have witnessed a rapidly rising interest in Artificial Intelligence (AI) and related technologies. AI is expected to cause significant socio-economic and scientific changes in the short term. One of these changes is the replacement of low-skilled workforce with AI and automation, which in turn increases the need for more employees equipped with AI skills. The purpose of our AI Master Program is to address this need by training engineers and scientists of the approaching AI era. Besides pursuing academic careers, graduates of this program are also expected to take entrepreneurial steps to establish AI companies that can develop products with high added-value.

In accordance with the entrepreneurial and distinctive education and training philosophy of Ozyegin University, the AI Master Program offers a world-class education to train engineers and scientists, who can work interdisciplinary, while employing the latest scientific knowledge and technical skills in the field of AI.


Graduation Requirements for M.Sc. in Artificial Intelligence (Thesis)


Course Type

Min. Credits (ECTS)

Min. Number of Courses

Required Courses



Elective Courses



GSE 680 Graduate Study and Seminars for Research, Innovation and Ethics



AI 693 M.Sc. Thesis Study I in Artificial Intelligence



AI 694 M.Sc. Thesis Study II in Artificial Intelligence



AI 695 M.Sc. Thesis Study III in Artificial Intelligence



Total ECTS


Graduation Requirements for M.Sc. in Artificial Intelligence (Non-Thesis)


Course Type

Min. Credits (ECTS)

Min. Number of Courses

Required Courses



Elective Courses



GSE 680 Graduate Study and Seminars for Research, Innovation and Ethics



AI 675 M.Sc. Term Project in Artificial Intelligence



Total ECTS



Required Courses

AI 675 M.Sc. Term Project in Artificial Intelligence

AI 693 M.Sc. Thesis Study I in Artificial Intelligence

This course is the first stage of M.Sc. Thesis where the student proposes a research problem with his/her thesis advisor.

AI 694 M.Sc. Thesis Study II in Artificial Intelligence

This course is the second stage of M.Sc. thesis where the student conducts a research and develops a solution for the problem identified in the first stage.

AI 695 M.Sc. Thesis Study III in Artificial Intelligence

This course is the third and final stage of M.Sc. thesis where the student writes, publishes and presents his/her thesis work.

GSE 680 Graduate Study and Seminars for Research, Innovation and Ethics

Topics include research and innovation methods, research and publication ethics and integrity, research dissemination methods (publications, presentations) and, social, environmental and economic impact of research and legal issues (IPR).


Required Courses’ Pool

Students are required to take at least 2 courses for the master’s degree with thesis and at least 3 courses for the master’s degree without thesis. If they take more courses from the pool, they may substitute that courses as elective courses for their graduation requirements.

AI 510 Machine Learning Theory

AI 511 Brain Theory

AI 512 General Artificial Intelligence

AI 520 Multi Agent Systems

CS 545 Deep Reinforcement Learning

This course presents introductory material for a branch of machine learning, called Reinforcement Learning, that aims to build models that learn intelligent action planning. Reinforcement learning models are at present actively used in robot control, building artificial intelligence engines of computer games, and autonomous driving. The course approaches reinforcement learning from a deep learning perspective. It also covers practical aspects of constructing deep neural network architectures that can perform reinforcement learning.

CS 551 Introduction to Artificial Intelligence

The aim of this course is to introduce to the students main concepts and techniques of Artificial Intelligence (AI). Also, the course targets equipping the students with the ability of building intelligent computational systems. Major topics of the course include: intelligent agents, heuristic search, planning, constraint satisfaction, knowledge representation & reasoning, and machine learning.

CS 554 Introduction to Machine Learning and Artificial Neural Networks

Course introduces and teaches standard techniques in neural networks and machine learning

Topics to be covered;

•Concept of (machine) learning 

•Methods derived from mathematics (interpolation, error minimization etc.)

•Methods inspired from (real) neural networks of the brain (perceptron learning, Hopfield

  networks, feedforward networks etc.)

CS 558 Introduction to Statistical Machine Learning

This course covers modern statistical inference techniques from a machine learning perspective. The concept of prior knowledge is introduced and its principled incorporation into a learning model is discussed. Modern approximate inference techniques that lie at the heart of the present machine learning research are explained in great detail. The course culminates with the recent developments on how these techniques can be applied to perform deep learning.

CS 566 Introduction to Deep Learning

This course discusses fundamental deep learning subjects such as convolutional neural networks, supervised classification, logistic regression, cross entropy. It further analyzes numerical stability of training, its importance on measuring performance. Course also applies fine tuning performance parameters and introduce various methods for fast convergence of training.

MATH 512 Statistical Learning Theory

MATH 513 Stochastic Optimal Control

This course introduces the students to the theory of stochastic optimal control and the solution techniques of stochastic optimal control problems. Topics include Markov decision processes, dynamic programming principle, partially observed Markov decision processes, Linear quadratic Gaussian problem and Kalman filtering, discounted and average cost Markov decision processes, team decision theory and decentralized control.


Elective Courses’ Pool

Students are required to take at least 5 courses for the master’s degree with thesis and at least 6 courses for the master’s degree without thesis. If students want to take a course outside this pool, they apply to the Institute Executive Board and can take the course approved by the Institute Executive Board and count it as an elective course.

EE 522 Digital Speech Processing

Topics include speech signal analysis, speech coding, text-to-speech synthesis, speech recognition, voice authentication techniques,  basics of linguistics, hearing, and sound propagation.

EE 525 Machine Learning

Topics include linear regression and classification concepts and techniques, Gaussian mixture models and Expectation Maximization algorithm, probabilistic principal component and factor analysis, support vector machines and multi-classifier methods, decision trees and random forests.

CS 511 Introduction to Robot Programming

The topics include introduction of robotic fundamentals (kinematics, dynamics, trajectory planning, control) and use of these knowledge to control and program robots, and robot learning.  

CS 515 Research methods in human-machine interaction

The topics include introduction of basic human-machine interaction scenarios and existing research, experiment design methodology, and experimental analysis techniques used in human-machine studies.  

CS 523 Computer Vision

This course discusses cameras and projections, feature detection and matching, stereo vision and multiple view geometry, motion estimation, object tracking, principle component analysis and some other necessary machine learning tools such as clustering and segmentation.

CS 550 Distributed Systems and Cloud Computing

In this course students learn advanced principles of distributed systems and cloud computing including different architectural styles, inter-process communication, virtualization, naming, distributed synchronization and consensus, replication and consistency, fault tolerance, and security issues. They gain practical skills on designing, implementing or using large-scale infrastructure and platform cloud services. Topics covered also include Internet of Things (IoT), Amazon Web Services, IBM Bluemix, and Microsoft Azure, Apache Hadoop, and Google Cloud service.

CS 552 Data Science with Python

This course aims to equip graduate students with the practical Machine Learning skills to solve real-life Data Science problems using existing tools and Python programming language. The following topics will be covered during the lectures:

•            Introduction to Python

•            Machine Learning with Python

•            Unsupervised Learning

•            Supervised Learning

•            Recommender Systems

CS 560 Information Retrieval and Search Engines

This course studies the theory, design, implementation and evaluation of text-based information retrieval systems. Topics include search engine architectures, crawling, indexing, retrieval models, ranking algorithms, link-based algorithms, clustering and classification, evaluation methods and text mining.

CS 562 Collective Decision Making in Multiagent Systems

This course provides an overview of collective decision making within multiagent systems and its main concepts, theories, and algorithms. It covers utility theory, game theory, preference aggregation, voting methods, principles of automated negotiation, and (group) recommender systems.

CS 567 Advanced Deep Learning

This course aims to advance the basic practical knowledge of Deep Learning to the level of understanding and advancing state of the art approaches for Deep Learning.

MATH 503 Numerical Linear Algebra

This course covers selected topics in numerical linear algebra. Topics include matrix analysis, direct methods for linear systems, iterative methods for linear systems, methods for eigenvalue problems, iterative methods for nonlinear systems.

MATH 506 Probability Theory

This course introduces the fundamental concepts of probability theory based on measure and integration theory. The course is split into three parts. The first part is on the basics of measure and integration, along with the notion of independence. The second part includes more advanced topics of measure and integration that are central to probability theory such as moments,

-spaces, types of convergence, Radon-Nikodym theorem, product spaces and Fubini’s theorem. The last part includes the notions of weak convergence, characteristic functions, and conditional expectation, and covers basic ‘successes’ of probability theory such as laws of large numbers, central limit theorem, and martingale convergence theorem.

MATH 514 Information Theory

MATH 515 Stochastic Analysis

DS 581 Data Mining 

The purpose of this course is to supplement students with the basic knowledge of data mining techniques. These techniques include descriptive ones such as clustering, association analysis and sequence analysis and, predictive ones such as decision trees and logistic regression. The theoretical lectures are coupled by applied studies where the necessary skills for using a data mining software package are given. By the end of the course, the students are able to identify the real life problems where a data mining approach is useful and apply some of the alternative techniques that can be used to solve those problems. 

IE 501 Linear Programming and Extensions

This course aims to provide students with a sound theoretical background on linear optimization and its extensions in other optimization areas.  It builds upon previously acquired introductory knowledge on Linear Programming (LP), which includes development of LP models and the workings of the Simplex Algorithm.  Students develop a deeper understanding of the mathematical underpinnings of the Simplex Algorithm and linear optimization in general. Topics include optimality conditions, duality theory, and methods for large scale optimization.

IE 502 Integer Programming

The aim of this course is to introduce both the practice and the theory of integer programming. The course covers MILP models, branch and bound, polyhedral theory, cutting plane algorithms, branch and cut, branch and price, Benders’ decomposition, and relaxations. In addition, students are expected to work on a project.

IE 503 Non-Linear Optimization

IE 504 Heuristic Optimization Methods

This course is based on modeling mathematical optimization problems and solving these problems using heuristic methods. The course will mainly address fundamental linear and integer linear optimization problems in IE/OR, but extensions shall also be discussed. Heuristic methods will be coded in the computer environment (e.g., Octave).

IE 532 Stochastic Models

This course provides an introduction to stochastic modeling. It covers various topics including conditional probability and expectation, discrete and continuous time Markov chains, Poisson process, queueing theory, and Markov Decision Processes

IE 543 Optimization Under Uncertainty

This course is based on modeling uncertain mathematical optimization problems and solving these problems using exact or approximation methods. The course mainly addresses robust and stochastic optimization modeling and solution methodologies. Solution methods (exact or approximation) are coded in the computer environment using, MATLAB, YALMIP, and CPLEX.

IE 562 Game Theory

Game theory is a mathematical tool developed to understand not only economical market participants’ interactions but also social phenomena observed as a result of these interactions. The aim of this course is to introduce the students the analytical approaches to investigate the strategic interactions of the participants.  Topics to be covered include, utility concept, games in normal form, dominance, Nash equilibrium, pure and mixed strategies, extensive form games, sequential games, games under asymmetric information. Students will be familiarized with the applications of the basic concepts in the fields of engineering, management and economics via case studies.

ME 589 Advanced Engineering Mathematics

This is an advanced course on engineering mathematics. Topics include mathematical modeling of nonlinear systems and linearization, batch processes, data-driven mathematical modeling, numerical mathematics, Kalman filter-based data processing, optimality, and vector calculus. Students make use of these techniques for their projects via MATLAB and Simulink.