COMPUTER ENGINEERING (MASTER) (THESIS)
Qualification Awarded Program Süresi Toplam Kredi (AKTS) Öğretim Şekli Yeterliliğin Düzeyi ve Öğrenme Alanı
2 120 FULL TIME TYÇ, TR-NQF-HE, EQF-LLL, ISCED (2011):Level 7
QF-EHEA:Second Cycle
TR-NQF-HE, ISCED (1997-2013): 48,52

Ders Genel Tanıtım Bilgileri

Course Code: 3016002050
Ders İsmi: Natural Language Processing
Ders Yarıyılı: Spring
Ders Kredileri:
Theoretical Practical Labs Credit ECTS
3 0 0 3 6
Language of instruction: TR
Ders Koşulu:
Ders İş Deneyimini Gerektiriyor mu?: No
Other Recommended Topics for the Course:
Type of course: Anabilim Dalı/Lisansüstü Seçmeli
Course Level:
Master TR-NQF-HE:7. Master`s Degree QF-EHEA:Second Cycle EQF-LLL:7. Master`s Degree
Mode of Delivery: Face to face
Course Coordinator : Dr.Öğr.Üyesi Recep DURANAY
Course Lecturer(s):
Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives: Natural Language Processing, a sub-branch of artificial intelligence, aims to develop techniques for processing the language that people use.
Course Content: By using these techniques, applications are developed in many subjects such as increasing human-machine communication, machine translation, rapid information extraction. The aim of this course is to introduce the basic techniques in Natural Language Processing and some of the current research on this subject.

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
1) Comprehensive Understanding of NLP Fundamentals: Students will acquire a deep understanding of the fundamental concepts and techniques used in Natural Language Processing. They will be able to explain the challenges in processing human language, the importance of tokenization, and various text preprocessing techniques.
2) Practical Proficiency in NLP Techniques: Students will gain hands-on experience in implementing key NLP techniques, such as sentiment analysis, named entity recognition, and text summarization. They will develop the ability to apply text representation methods like word embeddings and language modeling to solve real-world text understanding tasks.
3) Application of Deep Learning for NLP: Students will explore deep learning approaches in NLP, including recurrent neural networks (RNNs) for text generation and convolutional neural networks (CNNs) for text classification. They will learn how to build sequence-to-sequence models for machine translation and apply attention mechanisms in NLP tasks.
Field Specific Competence
1) Ethical Considerations in NLP: Students will examine the ethical implications of NLP applications, including privacy concerns, bias in language models, and social impact. They will be equipped to critically assess the ethical dimensions of NLP projects and make responsible decisions in their applications.
2) Research and Innovation in NLP: Students will be exposed to cutting-edge research and emerging trends in NLP. They will have the opportunity to explore advanced topics such as topic modeling, NLP for social media, and the latest developments in machine comprehension models for question answering.
Competence to Work Independently and Take Responsibility

Ders Akış Planı

Week Subject Related Preparation
1) Introduction to Natural Language Processing - Overview of NLP and its applications - Challenges in NLP: Ambiguity, context, and syntax - Tokenization and text preprocessing Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition / Daniel Jurafsky, James H. Martin
2) Text Representation and Feature Engineering - Bag-of-Words model and TF-IDF - Word embeddings (Word2Vec, GloVe) - Pre-trained language models (BERT, GPT) Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition / Daniel Jurafsky, James H. Martin
3) Language Modeling and Text Generation - N-gram language models - Recurrent Neural Networks (RNNs) for text generation - Sequence-to-Sequence models and attention mechanisms Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition / Daniel Jurafsky, James H. Martin
4) Part-of-Speech Tagging and Named Entity Recognition - POS tagging algorithms - Named Entity Recognition (NER) techniques Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition / Daniel Jurafsky, James H. Martin
5) Sentiment Analysis - Sentiment analysis approaches: Rule-based, ML, and deep learning - Handling sentiment in different languages Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition / Daniel Jurafsky, James H. Martin
6) Text Classification - Naive Bayes Classifier - Support Vector Machines (SVM) for text classification - Text classification with deep learning (CNN, LSTM) Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition / Daniel Jurafsky, James H. Martin
7) Text Summarization - Extractive summarization techniques - Abstractive summarization using seq2seq models Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition / Daniel Jurafsky, James H. Martin
8) Midterm exam
9) Text Translation - Machine Translation fundamentals - Neural Machine Translation (NMT) using attention Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition / Daniel Jurafsky, James H. Martin
10) Named Entity Linking and Coreference Resolution - Named Entity Linking (NEL) approaches - Coreference resolution algorithms Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition / Daniel Jurafsky, James H. Martin
11) Dependency Parsing and Constituency Parsing - Dependency parsing methods - Constituency parsing techniques Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition / Daniel Jurafsky, James H. Martin
12) Information Retrieval and Question Answering - TF-IDF for information retrieval - Question Answering with machine comprehension models Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition / Daniel Jurafsky, James H. Martin
13) Advanced NLP Topics - Topic modeling (LDA) - NLP for social media and web text - Ethical considerations in NLP Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition / Daniel Jurafsky, James H. Martin
14) Final

Sources

Course Notes / Textbooks: Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition / Daniel Jurafsky, James H. Martin
References: Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition / Daniel Jurafsky, James H. Martin

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları

1

2

3

4

5
Program Outcomes
1) Ability to reach wide and deep knowledge through scientific research in the field of Computer Science and Engineering, evaluate, interpret and apply.
2) Ability to use scientific methods to cover and apply limited or missing knowledge, and to integrate the knowledge of different disciplines.
3) Ability to construct Computer Science and Engineering problems, develop methods to solve the problems and use innovative methods in the solution.
4) Ability to develop new and/or original ideas and algorithm; develop innovative solutions in the design of system, component or process.
5) Ability to have extensive knowledge about current techniques and methods applied in Computer Engineering and their constraints.
6) Ability to design and implement analytical modeling and experimental research, solve and interpret complex situations encountered in the process.
7) Ability to use a foreign language (English) at least at the level of European Language Portfolio B2 in verbal and written communication.
8) Ability to lead in multidisciplinary teams, develop solutions to complex situations and take responsibility.
9) Awareness of the social, legal, ethical and moral values, and the ability to conduct research and implementation work within the framework of these values.
10) Awareness of the new and emerging applications in Computer Science and Engineering field, and the ability to examine them and learn if necessary.

Ders - Öğrenme Kazanımı İlişkisi

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Ability to reach wide and deep knowledge through scientific research in the field of Computer Science and Engineering, evaluate, interpret and apply. 4
2) Ability to use scientific methods to cover and apply limited or missing knowledge, and to integrate the knowledge of different disciplines. 4
3) Ability to construct Computer Science and Engineering problems, develop methods to solve the problems and use innovative methods in the solution. 4
4) Ability to develop new and/or original ideas and algorithm; develop innovative solutions in the design of system, component or process. 4
5) Ability to have extensive knowledge about current techniques and methods applied in Computer Engineering and their constraints. 3
6) Ability to design and implement analytical modeling and experimental research, solve and interpret complex situations encountered in the process. 2
7) Ability to use a foreign language (English) at least at the level of European Language Portfolio B2 in verbal and written communication. 3
8) Ability to lead in multidisciplinary teams, develop solutions to complex situations and take responsibility. 4
9) Awareness of the social, legal, ethical and moral values, and the ability to conduct research and implementation work within the framework of these values. 4
10) Awareness of the new and emerging applications in Computer Science and Engineering field, and the ability to examine them and learn if necessary. 5

Öğrenme Etkinliği ve Öğretme Yöntemleri

Anlatım
Course
Homework

Ölçme ve Değerlendirme Yöntemleri ve Kriterleri

Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama)
Homework

Assessment & Grading

Semester Requirements Number of Activities Level of Contribution
Homework Assignments 2 % 30
Midterms 1 % 30
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 3 14 42
Study Hours Out of Class 14 6 84
Homework Assignments 2 30 60
Midterms 1 2 2
Final 1 3 3
Total Workload 191