Course Name: MTH101 Mathematics I

Lecture Hours and ECTS:(4 - 0)  6
Course Description: Functions, limits and continuity. Derivatives. Mean value theorem. Sketching graphs. Definite integrals, infinite integrals (antiderivatives). Logarithmic, exponential, trigonometric and inverse trigonometric functions and their derivatives. L’Hospital’s rule. Techniques of integration. Applications of the definite integral, improper integrals.

Course Name: PHY101 General Physics I
Lecture Hours and ECTS:(4 - 2)   5
Course Description: Measurement, Estimating, Kinematics in one Dimension, Vectors, Newton’s Laws of Motion, Application of Newton’s Laws, Work and Energy, Conservation of Energy, Linear Momentum and Collisions.

Course Name: CHM101 General Chemistry
Lecture Hours and ECTS:(4 - 2)   5
Course Description: Introduction to basic principles of chemistry, atomic structure, molecule and ions, chemical reactions and balancing chemical reactions, precipitation reactions. Acid-Base reactions, redox reactions and balancing. Redox reactions. Stoichiometric relationships in chemical reactions, concentration and dilution, Acid base titration, redox titration. Gases.

Course Name: ENG101 English I
Lecture Hours and ECTS:(3 - 0)  3
Course Description: This first-year course focuses on the skills of academic reading, writing, listening and speaking. It revolves around thematic modules and aims at developing critical thinking skills, which enable students to become confident lifelong learners. It is offered in fall and summer terms.

Course Name: MTH113 Linear Algebra
Lecture Hours and ECTS:(3 - 0)  5
Course Description: This course aims to give details of Linear Algebra to students. Matrices and Systems of Equations, Determinants, Vector Spaces, Linear Transformations, Orthogonality, Eigenvalues, Numerical Linear Algebra will be thought during the semester. 

Course Name: YİT101 Turkish for Foreign Students I
Lecture Hours and ECTS:(2 - 0)  2
Course Description: The aim of this course is to introduce Turkish Language for Foreign Students of NEU. Fundamentals of Turkish phonology, simple sentence structures, vocabulary, simple sentence structure of Turkish, case endings and certain structures necessary for fluent communication, tenses and possessive constructions, reading articles and essays written in Turkish will be thought during the semester.

Course Name: ECC102 Programming and Problem Solving
Lecture Hours and ECTS:(4 - 0)  4
Course Description: This course provides an introduction to fundamental concepts of programming and use of built-in data structures in solving problems using the Python general-purpose programming language. this course, students study how write user-defined functions using iteration as well as recursion in Python. This course also stresses the importance of programming tools such as programming editors and debuggers. The students are expected to work within a GNU/Linux environment. The course provides a basic introduction into object-oriented programming.

Course Name:  MTH102 Mathematics II
Lecture Hours and ECTS:(4 - 0)   6
Course Description: Course content: Plane and polar co-ordinates, area in polar co-ordinates, arc length of curves. Limit, continuity and differentiability of function of several variables, extreme values, method of Lagrange multipliers. Double Integral, triple integral with applications. Line integrals, Green’s theorem. Sequences, infinite series, power series, Taylor’s series. Complex numbers.

Course Name:  PHY102 General Physics II
Lecture Hours and ECTS:(4 - 2)   7
Course Description: Centre of Mass, Rotation About a Fixed Axis ( angular quantities, kinematic equations, torque, moment of inertia, rotational kinetic energy), General Rotation, (the torque vector, angular momentum, conservation of angular momentum) Static Equilibrium, Elasticity and Fracture (statics, stability and balance, elasticity, stress, strain, fracture, trusses and bridges, arches and domes), Fluids (density, pressure, Pascal’s principle, bouyancy and Archimedes principles, fluids in flow, Bernoulli’s equation).

Course Name:  ENG102 English II
Lecture Hours and ECTS:(3 - 0)  3
Course Description: This thematic integrated course builds on EAP 1 by further improving students' reading, writing, listening and speaking skills in academic contexts. It is offered in the spring and summer terms.

Course Name:  ECC108 Object Oriented Programming
Lecture Hours and ECTS:(3 - 0)  6
Course Description: This course provides an in-depth discussion of object-oriented programming and how object oriented programming can be used in solving real-life problems. This course requires a more advanced use of programming tools (mainly editors and debuggers) that were introduced in ECC102 (Programming and Problem Solving). This course uses Python 3 to teach the fundamental concepts of object-oriented programming. The students are expected to work within a GNU/Linux environment. The course builds upon the knowledge of ECC102 and ECC201 and is the third course in line that uses Python as programming language.

Course Name:  ECC104 Discrete Structures
Lecture Hours and ECTS:(3 - 0)  6
Course Description: This course aims to introduce students about discrete structures. Sets and Logic, Proofs, Functions, Sequences and Relations, Algorithms, Introduction to Number Theory, Counting Methods and the Pigeonhole Principle, Recurrence Relations, Graph Theory, Trees, Network Models, Boolean Algebras and Combinatorial Circuits, Automata, Grammars and Languages, Computational Geometry will be thought during the semester.

Course Name: YİT102 Turkish for Foreign Students II
Lecture Hours and ECTS:(2 - 0)  2
Course Description: The aim of this course is to introduce Turkish Language for Foreign Students of NEU. Fundamentals of Turkish phonology, simple sentence structures, vocabulary, simple sentence structure of Turkish, case endings and certain structures necessary for fluent communication, tenses and possessive constructions, reading articles and essays written in Turkish will be thought during the semester.

Course Name:  MTH201 Differential Equations
Lecture Hours and ECTS:(4 - 0)   6
Course Description: Introducing first, second and higher order differential equations, and the methods of solving these equations. Emphasizing  the  important  of  differential  equations and  its  engineering  application.  Introducing  the Laplace  transform  and  its  applications  in  solving  differential  equations  and  other  engineering applications. Introducing the series method in solving differential equations.

Course Name:  ECC201 Data Structures and Algorithms
Lecture Hours and ECTS:(4 - 2)   6
Course Description: This course comprises an introductory exploration into the design and implementation of Abstract Data Types (ADTs) along with the study of algorithm design and complexity analysis. Even though the discussions during lectures about ADTs are language independent, this course uses Python, a very high-level general programming language, to implement these ideas using object-oriented programming. This class starts with a brief introduction to object-oriented programming.

Course Name:  ECC001 Logic Design
Lecture Hours and ECTS:(4 - 2)   6
Course Description: The aim of this course is to give the basics of Digital Logic Systems. Digital Systems and Information, Combinational Logic Circuits, Combinational Logic Design, Arithmetic Functions and HDLs, Sequential Circuits, Selected Design Topics, Registers and Register Transfers, Memory Basics, Computer Design Basics, Instruction Set Architecture, RISC and CISC Processors, Input-Output and Communication, Memory Systems will be thought during the semester.

Course Name:  ECC007 Multimedia Systems
Lecture Hours and ECTS:(3 - 2)   5
Course Description: The aim of this course is to introduce students about the Multimedia Systems. Introduction to Computer Science and Media Computation, Introduction to Programming in Jython, Modifying Pictures Using Loops, Modifying Pixels in a Range, Advanced Picture Techniques, Modifying Sounds Using Loops, Modifying Samples in a Range, Making Sounds by Combining Pieces, Building Bigger Programs, Creating and Modifying Text, Advanced Text Techniques:Web and Information, Making Text for theWeb, Creating and Modifying Movies, Speed, Functional Programming, Object-Oriented Programming will be thought during the semester.

Course Name:  AIE201 AI: Principles and Techniques
Lecture Hours and ECTS:(3 - 0)   5
Course Description: What do web search, speech recognition, face recognition, machine translation, autonomous driving, and automatic scheduling have in common? These are all complex real-world problems, and the goal of artificial intelligence (AI) is to tackle these with rigorous mathematical tools. In this course, students will learn the foundational principles that drive these applications and practice implementing some of these systems.

Course Name:  AIT103 Principles of Atatürk and the History of Revolution I
Lecture Hours and ECTS:(2 - 0)   2
Course Description: Beside discussing the definition of the term “ Revolution” by giving some examples such as French and Russian Revolutions, this course mainly focuses on the historical process that laid the basis of the foundation of Modern Turkey.In this context, after presenting a concise political history of the Ottoman Empire and its state mechanism, the political, social and economical developments between the Sultan Selim III Period (1789-1808) and the proclamation of Republic of Turkey by Mustafa Kemal Ataturk in 1923, are examined.

Course Name:  ECC202 Database Management Systems
Lecture Hours and ECTS:(4 - 0)   6
Course Description: This course comprises an introductory exploration into the design and implementation of database systems. Relational Data Model and SQL, Conceptual Modeling and Database Design, Models, Database Programming Techniques, Database Normalization Theory, File Structures-Indexing and Hashing, Query Processing-Optimization and Database Tuning, Transaction Processing-Concurrency Control and Recovery, Security and Distribution, Advanced Database Models-Systems and Applications will be thought during the semester.

Course Name:  AIE204 Neural Computation
Lecture Hours and ECTS:(3 - 2)   5
Course Description: In this course the computations carried out by the nervous system will be studied. Unlike most courses and artificial intelligence, a bottom-up approach will be taken. Apart from learning about the brain, numerical modelling of differential equations, non-linear dynamics, current neurobiological research and pitfalls in modelling real-world systems will be thought during the semester.

Course Name:  AIE206 Reasoning and Agents in AI
Lecture Hours and ECTS:(4 - 0)   6
Course Description: This course focuses on approaches relating to representation, reasoning and planning for solving real world inference. The course illustrates the importance of (i) using a smart representation of knowledge such that it is conducive to efficient reasoning, and (ii) the need for exploiting task constraints for intelligent search and planning. The notion of representing action, space and time is formalized in the context of agents capable of sensing the environment and taking actions that affect the current state. There is also a strong emphasis on the ability to deal with uncertain data in real world scenarios.

Course Name:  MTH251 Probability and Statistics
Lecture Hours and ECTS: (3 - 0)   6
Course Description: The aim of this course is to give details of probability to students. Statistics, Data and Statistical Thinking, Methods for Describing Sets of Data, Probability, Random Variables and Probability Distributions, Inferences Based on Samples, Design of Experiments and Analysis of Variance, Categorical Data Analysis, Simple Linear Regression, Multiple Regression and Model Building, Methods for Quality Improvement: Statistical Process Control, Time Series, Nonparametric Statistics will be thought during the semester.

Course Name:  AIT104 Principles of Atatürk and the History of Revolution II
Lecture Hours and ECTS:(2 - 0)   2
Course Description: The political, social, economical and cultural transformation in the Republic of Turkey; The six principles of Atatürk and Kemalizm; Turkish Foreign Policy during the Atatürk period.

Course Name:  ECC302 Operating Systems
Lecture Hours and ECTS:(3 - 0)   7
Course Description: The aim of this course is to give details of operating systems and how they work to students. Principles of operating systems. Memory management. Multiprocessing. Virtual memory concepts. Memory protection. Scheduling. Process management. Time-slicing and priorities, deadlocks and process synchronization. Peripheral control. Filing system management. Resource control and monitoring. Linux and Windows Operating Systems will be covered during the semester.

Course Name:  AIE301 Pattern Recognition
Lecture Hours and ECTS:(3 - 2)   7
Course Description: A pattern recognition system can be designed based on a number of different approaches: (i) template matching, (ii) geometric (statistical) methods, (iii) structural (syntactic) methods, and (iv) neural (deep) networks. This course will introduce the fundamentals of statistical pattern recognition with examples from several application areas. The course will cover techniques for visualizing and analyzing multi-dimensional data along with algorithms for projection, dimensionality reduction, clustering and classification.

Course Name:  ENG201 Oral Communication Skills
Lecture Hours and ECTS:(3 - 0)   5
Course Description: The aim of the course is to provide techniques for dealing with academic prose.
Definition of Technical Communication, Profiling Audiences, The Technical Communication Process, Technical Communication Style, Researching, Designing Pages, Using Visual Aids, Summarizing, Defining, Describing, Sets of Instructions, Memorandums and Informal Reports, Developing Websites, Formal Reports, Recommendation and Feasibility Reports, Proposals, User Manuals, Oral Presentations, Letters, Job Application Materials will be tought during the semester.

Course Name:  AIE303 Natural Language Processing
Lecture Hours and ECTS:(4 - 2)   7
Course Description: The intent of the course is to present a fairly broad graduate-level introduction to Natural Language Processing (NLP, a.k.a. comptuational linguistics), the study of computing systems that can process, understand, or communicate in human language. The primary focus of the course will be on understanding various NLP tasks, algorithms for effectively solving these problems, and methods for evaluating their performance. There will be a focus on statistical and neural-network learning algorithms that train on (annotated) text corpora to automatically acquire the knowledge needed to perform the task.

Course Name:  ECC439 Occupational Health and Safety I
Lecture Hours and ECTS:(2 - 0)   4
Course Description: The aim of the course is to introduce students about safety applications in real life practices. Occupational Health and Historical Development of Safety, Occupational Health and Purpose and Importance of Safety, Occupational Health and Safety Concepts in the area, Overview of the Occupational Health and Safety, work accidents, occupational diseases, to be taken against the Work Accidents and Occupational Diseases precautions, accidents at work and Costs arising from occupational diseases will be thought.

Course Name:  AIE302 Introduction to Machine Learning
Lecture Hours and ECTS:(4 - 2)   6
Course Description: This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

Course Name:  ECC303 Data Communication and Networking
Lecture Hours and ECTS:(4 - 2)   6
Course Description: The aim of this course is to give details of computer networking and data communications. Introduction to Computer Networks and Data Communications, Fundamentals of Data and Signals, Conducted and Wireless Media, Making Connections, Making Connections Efficient, Errors, Error Detection and Error Control, Local Area Networks, Introduction to Metropolitan Area Networks and Wide Area Networks, The Internet, Voice and Data Delivery Networks, Network Security, Network Design and Management subjects will be covered during the semester.

Course Name:  AIE304 Learning in Humans
Lecture Hours and ECTS:(3 - 0)   6
Course Description: The aim of this unit is to outline the basic learning mechanisms that allow us to organise our behaviour and adapt to our environment.

Course Name:  AIE306 Deep Learning
Lecture Hours and ECTS:(3 - 2)   6
Course Description: This course is an introduction to deep learning, a branch of machine learning concerned with the development and application of modern neural networks. basic neural networks, convolutional and recurrent network structures, deep unsupervised and reinforcement learning, and applications to problem domains like speech recognition and computer vision subjects will be covered.

Course Name:  ECC440 Occupational Health and Safety II
Lecture Hours and ECTS:(2 - 0)   4
Course Description: The aim of the course is to introduce students about safety applications in real life practices. Occupational Health and Historical Development of Safety, Occupational Health and Purpose and Importance of Safety, Occupational Health and Safety Concepts in the area, Overview of the Occupational Health and Safety, work accidents, occupational diseases, to be taken against the Work Accidents and Occupational Diseases precautions, accidents at work and Costs arising from occupational diseases.

Course Name: AIE401 Introduction to Robotics
Lecture Hours and ECTS:(3 - 2)   7
Course Description: The aim of this course is to introduce the student about the basic components of Robotics. The subjects that will be covered during the semester are: Basic components of robot systems; coordinate frames, homogeneous transformations, kinematics for manipulator, inverse kinematics; manipulator dynamics, Jacobians: velocities and static forces , trajectory planning, Actuators, Sensors, Vision, Fuzzy logic control of manipulator and robotic programming.

Course Name:  AIE403 Computer Vision
Lecture Hours and ECTS:(3 - 2)   7
Course Description: This course provides an introduction to computer vision, including fundamentals of image formation, camera imaging geometry, feature detection and matching, stereo, motion estimation and tracking, image classification, scene understanding, and deep learning with neural networks.

Course Name:  AIE491 Senior Project I
Lecture Hours and ECTS:(3 - 0)   6
Course Description: The aim of the course is to give senior design experience to students. This course is the first part of design project. The senior design project can be a project under the supervision of a faculty member. Oral presentations and written reports are required.

Course Name:  AIE492 Senior Project II
DersinHaftalık Ders Uygulama Saati ve AKTS:(4 - 0)   7
Course Description: The aim of the course is to give senior design experience to students. This course is the second and final part of design project. Oral presentations and written reports are required.

Course Name: ECC429 Engineering Ethics
Lecture Hours and ECTS:(3 - 0)   6
Course Description: The aim of the course is to provide knowledge about engineering ethics. An Overview of Ethics, Ethics for IT Professionals, Computer and Internet Crime, Privacy, Freedom of Expression, Intellectual Property, Software Development, The Impact of AI on the Quality of Life, Social Networking, Ethics of AI and IT Organizations.

Course Name: AIE402 Speech Processing
Lecture Hours and ECTS:(3 - 2)   7
Course Description: Speech Processing offers a practical and theoretical understanding of how human speech can be processed by computers. It covers speech recognition, speech synthesis and spoken dialog systems. The course involves practicals where the student will build working speech recognition systems, build their own synthetic voice and build a complete telephone spoken dialog system.

TECHNICAL ELECTIVE COURSES

Course Name:  AIE411 Advanced Data Analysis
Lecture Hours and ECTS:(3 - 2)  5
Course Description: In this course a number of advanced and multivariate data analysis methods will be introduced and lead students through their background, rationale, practical application and interpretation, using step by step explanations. Knowledge of these will help students understand, evaluate and draw informed conclusions from the complex analyses of other researchers, address how they will utilise the data in projects.

Course Name:  AIE412 Search Engines
Lecture Hours and ECTS:(3 - 2)   5
Course Description: Information retrieval is the process through which a computer system can respond to a user's query for text-based information on a specific topic. IR was one of the first and remains one of the most important problems in the domain of natural language processing (NLP). Web search is the application of information retrieval techniques to the largest corpus of text anywhere -- the web -- and it is the area in which most people interact with IR systems most frequently.

Course Name: AIE413 Human-Robot Interaction
Lecture Hours and ECTS:(3 - 2)   5
Course Description: This course will focus on the emerging field of Human-Robot Interaction (HRI). This multidisciplinary research area draws primarily from: robotics, AI, human-computer interaction, and cognitive psychology. The primary goal of HRI is to enable robots to successfully interact with humans. This course will cover a variety of topics related to social intelligence: learning, teamwork, planning, dialog, emotion, embodied intelligence, among others. For each topic, readings and lectures will cover (1) what’s known about how this ability arises in human intelligence, and (2) state-of-the-art approaches to building computational systems with this type of social intelligence. Assignments will be a combination of readings, discussions, team problem solving sessions, and a team final project involving the implementation of a Human-Robot Interaction system.

Course Name: AIE414 Deep Reinforcement Learning and Control
Lecture Hours and ECTS:(3 - 2)   5
Course Description: Aim of this course is to implement and experiment with existing algorithms for learning control policies guided by reinforcement, demonstrations and intrinsic curiosity. Evaluate the sample complexity, generalization and generality of these algorithms. Be able to understand research papers in the field of robotic learning. Try out some ideas/extensions on your own. Particular focus on incorporating sensory input from visual sensors.

Course Name: AIE415 Mobile Robot Programming
Lecture Hours and ECTS:(3 - 2)   5
Course Description: This course is an extensive hands-on introduction to the concepts and basic algorithms needed to make a mobile robot function reliably and effectively. This is a lab course with emphasis on hands-on learning. Students will get experience in this course in addition to some theory. Lectures are focused on the content of the next lab. There is a sequence of labs and they build on each other. The course will culminate with an individually implemented project. Students will also be introduced to the basics of doing research in this course.

Course Name: AIE416 Autonomous Agents
Lecture Hours and ECTS:(3 - 2)   5
Course Description: The course aims at giving the students an understanding of design principles for autonomous systems, both robots and software agens, and also gives students the opportunity to apply their knowledge in practice through the construction of a simple autonomous robot.

Course Name: AIE417 Introduction to Quantum Computing
Lecture Hours and ECTS:(3 - 0)   5
Course Description: This course aims to provide a first introduction to quantum computing. The paradigm change between conventional computing and quantum computing will be highlighted, and several basic quantum algorithms will be introduced. The implications of quantum computing on fields such as computer security and machine learning will be discussed.

Course Name: AIE418 Computer Animation & Visualization
Lecture Hours and ECTS:(3 - 2)   5
Course Description: In this course, Principles of interactive computer graphics; Topics include fundamental techniques in graphics, graphic systems, graphic communication, geometric modeling, rendering, computer animation, visualization and virtual reality and other recent developments in computer graphics subjects will be covered during the semester.

Course Name: AIE419 Algorithmic Game Theory and its Applications
Lecture Hours and ECTS:(3 - 2)  5
Course Description: Broad survey of topics at the interface of theoretical computer science and economics. Introduction to auction and mechanism design, with an emphasis on computational efficiency and robustness. Introduction to the "price of anarchy", with applications to networks. Algorithms and complexity theory for learning and computing Nash and market equilibria. Case studies in Web search auctions, wireless spectrum auctions, matching markets, network routing, and security applications.

Course Name: AIE420 Fuzzy Systems
Lecture Hours and ECTS:(3 - 2)   5
Course Description: Fuzzy set theory, rules, reasoning and inference systems. Regression and optimization, derivative-based optimization – genetic algorithms, simulated annealing, Neural Networks, adaptive networks.

The students who successfully complete the program are awarded the degree of Bachelor of Science in Artificial Intelligence Engineering.

This is a First Cycle (Bachelor’s Degree) program.

In the framework of the regulations set by Higher Education Council of Turkey (YÖK), student admission for this undergraduate program is made through a university entrance examination called YKS. Following the submission of students’ academic program preferences, Student Selection and Placement Center (ÖSYM) places the students to the relevant program according to the score they get from ÖSYS.

International students are accepted to this undergraduate program according to the score of one of the international exams they take such as SAT, ACT and so on, or according to their high school diploma score.

Exchange student admission is made according to the requirements determined by bilateral agreements signed by NEU and the partner university.

Visiting students can enroll for the courses offered in this program upon the confirmation of the related academic unit. Additionally, they need to prove their English language level since the medium of instruction of the program is English.

The students studying in this undergraduate program are required to have a Cumulative Grade Points Average (CGPA) of not less than 2.00/4.00 and have completed all the courses with at least a letter grade of DD/S in the program in order to graduate. The minimum number of ECTS credits required for graduation is 251. It is also mandatory for the students to complete their compulsory internship in a specified duration and quality.

At Near East University, full-time students can be exempted from some courses within the framework of the related bylaws. If the content of the course previously taken in another institution is equivalent to the course offered at NEU, then the student can be exempted from this course with the approval of the related faculty/graduate school after the evaluation of the course content.

The program's goal is to equip its graduates with both the fundamental scientific principles that underpin the key artificial intelligence technologies in use today and the engineering skills that enable those principles to be applied in practice. Upon graduation, students should be equipped to pursue a career as artificial intelligence professionals or, if they so wish, to pursue further academic studies. The graduates will be professionals who can be flexible and integrate in a relatively short time into a wide-range of different sectors of the industry.

• To have adequate knowledge in Mathematics, Science and Artificial intelligence Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems.
• Gains the ability to understand the basic algorithms of artificial intelligence-based systems and understand the basic concepts of artificial intelligence-based systems.
• Gain knowledge of problem solving and planning.
• To be able to design artificial intelligence based systems.
• To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in Artificial Intelligence Engineering applications; to be able to use information technologies effectively.
• Intelligent agents, search methods in problem solving, informed and uninformed search methods, exploration methods, constraint supply problems, game playing, knowledge and reasoning, primary logic, knowledge representation, learning.
• To be able to solve real life problems involving large and complex data sets using mathematical computational and artificial intelligence techniques
• To have knowledge about global and social impact of Artificial Intelligence Engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of Artificial Intelligence Engineering solutions.
• To be aware of ethical behaviour, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications.
• To be able to represent information using different techniques
• To be able to the appropriate scanning technique to achieve the desired goals.
• To be able to collect data in the area of Artificial Intelligence Engineering, and to be able to communicate with colleagues in a foreign language. ("European Language Portfolio Global Scale", Level B1)
• To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively, to be able to give and receive clear and comprehensible instructions.

Course and Program Outcomes Matrix

Graduates of Artificial Intelligence Engineering program may work as a AI/Machine Learning Engineer or Developer. Also they can work in the Healthcare, Customer service, Airline industry, Cybersecurity, Education, Marketing, Retail and E-Commerce, Financial Markets and Services departments.

The students graduating from this program may apply to graduate programs.

Course Structure Diagram with Course Credits

Exam Regulations, Assessment and Grading

In order to graduate from this undergraduate program, the students are required;

• to succeed in all of the courses listed in the curriculum of the program by getting the grade of at least DD/S with a minimum of 251 ECTS
• to have a Cumulative Grade Point Average (CGPA) of 2.00 out of 4.00
• to complete their compulsory internship in a specified duration and quality.

This is a full time program.

Prof. Dr. Fadi AL-TURJMAN, Head of Department, Faculty of Engineering, Near East University

• Evaluation Survey
• Graduation Survey
• Satisfaction Survey