PhD in Computer Engineering with a master's degree
PhD	TR-NQF-HE: Level 8	QF-EHEA: Third Cycle	EQF-LLL: Level 8

General course introduction information

Course Code:

BIL608

Course Name:

Natural Language Processing

Course Semester:

Fall
Spring

Course Credits:

Theoretical	Practical	Credit	ECTS
3	0	3	10

Language of instruction:

Course Requisites:

Does the Course Require Work Experience?:

Type of course:

Department Elective

Course Level:

PhD

TR-NQF-HE:8. Master`s Degree

QF-EHEA:Third Cycle

EQF-LLL:8. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Prof. Dr. PINAR YILDIRIM

Course Lecturer(s):

Course Assistants:

Course Objective and Content

Course Objectives:	Introduction to Artificial Intelligence. Heuristic problem solving. State spaces. Serching at state spaces. Games. Minimum spanning tree. Knowledge modeling. Representing knowledge. Logic. Neural networks. Fuzzy Logic.
Course Content:	Introduction to Artificial Intelligence. Heuristic problem solving. State spaces. Serching at state spaces. Games. Minimum spanning tree. Knowledge modeling. Representing knowledge. Logic. Neural networks. Fuzzy Logic.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Knows fundamental concepts about artificial intelligence
2) Knows fundamental concepts about machine learning.
3) Knows search algorithms.
4) Knows fundamental concepts about game theory.
5) Knows fundamental comcepts about logic and reasoning.
2 - Skills
Cognitive - Practical
1) Able to use artificial neural networks for the modelling of real-world problems.
2) Able to model knowledge-based systems.
3) Able to use Prolog for logic programming in elementary level.
4) Able to design natural language processing systems using hidden markov model.
5) Able to design convolutional neural network system for computer vision.
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Lesson Plan

Week	Subject	Related Preparation
1)	Fundamental concepts of artificial intelligence
2)	Machine learning
3)	Artificial neural networks
4)	Game theory
5)	Problem solving: Uninformed search agents
6)	Problem solving: Informed search agents
7)	Knowledge-based agents: The Wumpus World
8)	Midterm
9)	Logic and reasoning
10)	Logic Programming: Prolog
11)	Fundamental concepts about voice and vision recognition
12)	Voice recognition using hidden markov models
13)	Image recognition based on convolutional neural network.
14)	Project presentations

Sources

Course Notes / Textbooks:	Artificial Intelligence: A Modern Approach. Stuart Russell, Peter Norvig, Prentice Hall, Second Edition Yapay Zeka, Prof.Dr.Vasif V.Nabiyev
References:	Yapay Zeka Geçmişi ve Geleceği, Nils J. Nilson Introduction to Algorithms, Cormen. Makine Öğrenmesi, Ethem Alpaydın

Course-Program Learning Outcome Relationship

Learning Outcomes	1	2	4	5	5	6	7	8	9	10
Program Outcomes
1) Information on project management and practices in business life such as risk management and change management; awareness about entrepreneurship, innovation and sustainable development.
2) Sufficient knowledge in mathematics, science and engineering related to their branches; the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems.
3) The ability to identify, define, formulate and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose.
4) The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. (Realistic constraints and conditions include such issues as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, according to the nature of design.)
5) Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively.
6) Ability to design experiments, conduct experiments, collect data, analyze and interpret results for the examination of engineering problems.
7) The ability to communicate effectively in Turkish both verbally and in writing; at least one foreign language knowledge.
8) Knowledge of the necessity of lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
9) Professional and ethical responsibility.
10) Information on the effects of engineering applications on health, environment and safety in the universal and social dimensions and the problems of the times; awareness of the legal consequences of engineering solutions.
11) The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Information on project management and practices in business life such as risk management and change management; awareness about entrepreneurship, innovation and sustainable development.
2)	Sufficient knowledge in mathematics, science and engineering related to their branches; the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems.
3)	The ability to identify, define, formulate and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose.
4)	The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. (Realistic constraints and conditions include such issues as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, according to the nature of design.)
5)	Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively.
6)	Ability to design experiments, conduct experiments, collect data, analyze and interpret results for the examination of engineering problems.
7)	The ability to communicate effectively in Turkish both verbally and in writing; at least one foreign language knowledge.
8)	Knowledge of the necessity of lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
9)	Professional and ethical responsibility.
10)	Information on the effects of engineering applications on health, environment and safety in the universal and social dimensions and the problems of the times; awareness of the legal consequences of engineering solutions.
11)	The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.

Learning Activity and Teaching Methods

Expression
Individual study and homework
Lesson
Problem Solving
Project preparation
Report Writing
Q&A / Discussion
Application (Modelling, Design, Model, Simulation, Experiment etc.)

Assessment & Grading Methods and Criteria

Written Exam (Open-ended questions, multiple choice, true-false, matching, fill in the blanks, sequencing)
Application
Individual Project
Presentation
Reporting
Bilgisayar Destekli Sunum

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Presentation	1	% 10
Project	1	% 30
Midterms	1	% 20
Final	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
total		% 100

Workload and ECTS Credit Grading

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	13	3	39
Presentations / Seminar	1	3	3
Project	1	48	48
Midterms	1	48	48
Paper Submission	1	12	12
Final	1	48	48
Total Workload			198