DEPARTMENT OF SOFTWARE ENGINEERING (ENGLISH)
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Ders Genel Tanıtım Bilgileri

Course Code:

1413002021

Ders İsmi:

Natural Language Processing

Ders Yarıyılı:

Spring

Ders Kredileri:

Theoretical	Practical	Credit	ECTS
3	0	3	5

Language of instruction:

Ders Koşulu:

Ders İş Deneyimini Gerektiriyor mu?:

Type of course:

Department Elective

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Dr.Öğr.Üyesi Adem ÖZYAVAŞ

Course Lecturer(s):

Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives:	To meet with natural language and its application areas and to perform applications related to the subject.
Course Content:	Morphological Analysis; Syntactic Analysis; Language and Language Structures; Regular Languages; Word Processing Algorithms; Machine Learning; Text Classification; Information Extraction; Access to Information; Question Answering Systems; Collocations

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
2 - Skills
Cognitive - Practical
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Ders Akış Planı

Week	Subject	Related Preparation
1)	Foundations of Statistical Natural Language Processing Part 1	Foundations of Statistical Natural Language Processing Bölüm 1
2)	Fundamentals of Linguistics	Foundations of Statistical Natural Language Processing Bölüm 3
3)	Grammar and Language Models	Foundations of Statistical Natural Language Processing Bölüm 11
4)	Morphological Analysis	Foundations of Statistical Natural Language Processing Bölüm 10
5)	Syntactic Analysis	Foundations of Statistical Natural Language Processing Bölüm 10
6)	Regular Languages - RegEx	Handbook of Natural Language Processing, R. Dale, H. Moisl, H.Somers, Marcel Dekker
7)	Machine Learning I	Introduction to Machine Learning, MIT
8)	Midterm
9)	Text Classification	Foundations of Statistical Natural Language Processing Bölüm 16
10)	Information Extraction	Foundations of Statistical Natural Language Processing Bölüm 15
11)	Access to Information	Foundations of Statistical Natural Language Processing Bölüm 15
12)	Question Answering Systems	Speech and Language Processing Bölüm 23
13)	collocation	Foundations of Statistical Natural Language Processing Bölüm 5
14)	Final

Sources

Course Notes / Textbooks:	Natural Language Understanding, J.Allen, Benjamin-Cummings Speech and Language Processing, Jurafsky and Martin, Prentice Hall Foundations of Statistical Natural Language Processing, C. D. Manning, H. Schütze, MIT Handbook of Natural Language Processing, R. Dale, H. Moisl, H.Somers, Marcel Dekker
References:	Natural Language Understanding, J.Allen, Benjamin-Cummings Speech and Language Processing, Jurafsky and Martin, Prentice Hall Foundations of Statistical Natural Language Processing, C. D. Manning, H. Schütze, MIT Handbook of Natural Language Processing, R. Dale, H. Moisl, H.Somers, Marcel Dekker

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları
Program Outcomes
1) Competent knowledge of mathematics, science and technology, and computer engineering; ability to apply this knowledge to engineering solutions.
2) Skills to design and conduct experiments, collect data, analyze and interpret results.
3) Ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; ability to apply modern design methods for this purpose.
4) Ability to develop, select and use modern techniques and tools required for analysis and solution of complex problems encountered in engineering practice; ability to use information technologies effectively.
5) Ability to design and conduct experiments, collect data, analyze and interpret results to investigate complex engineering problems or discipline-specific research topics.
6) Ability to work effectively in intra-disciplinary and multi-disciplinary teams; ability to work individually.
7) Ability to communicate effectively in Turkish, both orally and in writing; Knowledge of at least one foreign language; the ability to write and understand written reports effectively, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8) Awareness of the necessity of lifelong learning; the ability to access information, to follow developments in science and technology, and to constantly renew oneself.
9) Acting in accordance with ethical principles, professional and ethical responsibility awareness; information about standards used in engineering applications.
10) Information about business life practices such as project management, risk management and change management; awareness of entrepreneurship, innovation; information about sustainable development.
11) Knowledge about the universal and social effects of engineering applications on health, environment and safety and the problems of the age reflected in the field of engineering; awareness of the legal consequences of engineering solutions.

Ders - Öğrenme Kazanımı İlişkisi

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

	Program Outcomes	Level of Contribution
1)	Competent knowledge of mathematics, science and technology, and computer engineering; ability to apply this knowledge to engineering solutions.
2)	Skills to design and conduct experiments, collect data, analyze and interpret results.
3)	Ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; ability to apply modern design methods for this purpose.
4)	Ability to develop, select and use modern techniques and tools required for analysis and solution of complex problems encountered in engineering practice; ability to use information technologies effectively.
5)	Ability to design and conduct experiments, collect data, analyze and interpret results to investigate complex engineering problems or discipline-specific research topics.
6)	Ability to work effectively in intra-disciplinary and multi-disciplinary teams; ability to work individually.
7)	Ability to communicate effectively in Turkish, both orally and in writing; Knowledge of at least one foreign language; the ability to write and understand written reports effectively, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8)	Awareness of the necessity of lifelong learning; the ability to access information, to follow developments in science and technology, and to constantly renew oneself.
9)	Acting in accordance with ethical principles, professional and ethical responsibility awareness; information about standards used in engineering applications.
10)	Information about business life practices such as project management, risk management and change management; awareness of entrepreneurship, innovation; information about sustainable development.
11)	Knowledge about the universal and social effects of engineering applications on health, environment and safety and the problems of the age reflected in the field of engineering; awareness of the legal consequences of engineering solutions.

Öğrenme Etkinliği ve Öğretme Yöntemleri

Ölçme ve Değerlendirme Yöntemleri ve Kriterleri

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	2	% 10
Project	1	% 30
Seminar	1	% 10
Midterms	1	% 10
Semester Final Exam	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
total		% 100

İş Yükü ve AKTS Kredisi Hesaplaması

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	13	3	39
Study Hours Out of Class	13	3	39
Presentations / Seminar	1	15	15
Project	1	30	30
Homework Assignments	2	10	20
Midterms	1	5	5
Final	1	5	5
Total Workload			153