| Cyber Security (Master) (with Thesis) (English) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
| Course Code: | COE5027 | ||||
| Course Name: | Natural Language Process | ||||
| Semester: | Fall | ||||
| Course Credits: |
|
||||
| Language of instruction: | English | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | Yes | ||||
| Type of course: | Departmental Elective | ||||
| Course Level: |
|
||||
| Mode of Delivery: | Face to face | ||||
| Course Coordinator: | Doç. Dr. EMİR SEYYEDABBASİ | ||||
| Course Lecturer(s): | Assist. Prof. Dr. Alper Öner, Res. Assist. Yazım Beril Uluer | ||||
| Course Assistants: |
| Course Objectives: | Overall, the objective of an NLP lecture is to provide students or participants with a foundation in NLP concepts, techniques, and applications, enabling them to understand and work on various NLP tasks and contribute to advancements in the field. |
| Course Content: | Regular Expressions, Text Normalization, Edit Distance, N-gram Language Models, Naive Bayes and Sentiment Classification, Vector Semantics and Embeddings, Sequence Labeling for Parts of Speech and Named Entities, Transformers and Pretrained Language Models, Machine Translation, Question Answering and Information Retrieval, Chatbots and Dialogue Systems, Automatic Speech Recognition and Text-to-Speech |
|
The students who have succeeded in this course;
1) Students will understand the basic concepts of Natural Language Processing (NLP), challenges in processing language, and text preprocessing techniques. 2) Students will be able to use deep learning models such as RNN and CNN for text generation and classification, and apply advanced techniques such as machine translation and attention mechanisms. 3) Students will become familiar with ethical issues such as privacy, bias and social influences in NLP applications and will be able to make critical evaluations in these areas. 4) Students will gain insight into innovative applications by examining current NLP research areas such as topic modeling, social media analysis, and question-answer systems. |
| Week | Subject | Related Preparation |
| 1) | Introduction to Natural Language Processing | |
| 2) | Regular Expressions | |
| 3) | Text Normalization, Edit Distance | |
| 4) | N-gram Language Models | |
| 5) | Naive Bayes and Sentiment Classification | |
| 6) | Logistic Regression | |
| 7) | Vector Semantics and Embeddings | |
| 8) | Midterm Exam | |
| 9) | Sequence Labeling for Parts of Speech and Named Entities | |
| 10) | Transformers and Pretrained Language Models | |
| 11) | Machine Translation | |
| 12) | Question Answering and Information Retrieval | |
| 13) | Chatbots and Dialogue Systems | |
| 14) | Automatic Speech Recognition and Text-to-Speech |
| Course Notes / Textbooks: | Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems, by Sowmya Vajjala, Bodhisattwa Majumder Anuj Gupta, Harshit Surana, O Reilly, 2020. |
| References: | Speech and Language Processing (3rd ed. draft) Dan Jurafsky and James H. Martin. (https://web.stanford.edu/~jurafsky/slp3/) |
| Course Learning Outcomes | 1 |
2 |
3 |
4 |
||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Program Outcomes | ||||||||||||
| 1) Being able to develop and deepen their knowledge at the level of expertise in the same or a different field, based on undergraduate level qualifications. | ||||||||||||
| 2) To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field. | ||||||||||||
| 3) To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines. | ||||||||||||
| 4) To be able to solve the problems encountered in the field by using research methods. | ||||||||||||
| 5) To be able to systematically transfer current developments in the field and their own studies to groups in and outside the field, in written, verbal and visual forms, by supporting them with quantitative and qualitative data. | ||||||||||||
| 6) To be able to communicate orally and in writing using a foreign language at least at the B2 General Level of the European Language Portfolio. | ||||||||||||
| 7) To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning. | ||||||||||||
| 8) To be able to use information and communication technologies at an advanced level along with computer software at the level required by the field. | ||||||||||||
| 9) To be able to supervise and teach these values by observing social, scientific, cultural and ethical values in the stages of collecting, interpreting, applying and announcing the data related to the field. | ||||||||||||
| 10) To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies. | ||||||||||||
| 11) Being able to independently carry out a work that requires expertise in the field. | ||||||||||||
| 12) To be able to develop new strategic approaches for the solution of complex and unpredictable problems encountered in applications related to the field and to produce solutions by taking responsibility. | ||||||||||||
| No Effect | 1 Lowest | 2 Average | 3 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Being able to develop and deepen their knowledge at the level of expertise in the same or a different field, based on undergraduate level qualifications. | 2 |
| 2) | To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field. | |
| 3) | To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines. | 1 |
| 4) | To be able to solve the problems encountered in the field by using research methods. | |
| 5) | To be able to systematically transfer current developments in the field and their own studies to groups in and outside the field, in written, verbal and visual forms, by supporting them with quantitative and qualitative data. | |
| 6) | To be able to communicate orally and in writing using a foreign language at least at the B2 General Level of the European Language Portfolio. | 1 |
| 7) | To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning. | |
| 8) | To be able to use information and communication technologies at an advanced level along with computer software at the level required by the field. | |
| 9) | To be able to supervise and teach these values by observing social, scientific, cultural and ethical values in the stages of collecting, interpreting, applying and announcing the data related to the field. | 2 |
| 10) | To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies. | |
| 11) | Being able to independently carry out a work that requires expertise in the field. | |
| 12) | To be able to develop new strategic approaches for the solution of complex and unpredictable problems encountered in applications related to the field and to produce solutions by taking responsibility. |
| Semester Requirements | Number of Activities | Level of Contribution |
| Homework Assignments | 2 | % 20 |
| Project | 2 | % 40 |
| Final | 1 | % 40 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 60 | |
| PERCENTAGE OF FINAL WORK | % 40 | |
| total | % 100 | |
| Activities | Number of Activities | Workload |
| Course Hours | 14 | 42 |
| Presentations / Seminar | 3 | 25 |
| Project | 3 | 28 |
| Homework Assignments | 4 | 20 |
| Final | 2 | 20 |
| Total Workload | 135 | |