Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|
| NATURAL LANGUAGE PROCESSING | COED1112914 | Fall Semester | 3+0 | 3 | 8 |
| Prerequisites Courses | |
| Recommended Elective Courses | |
| Language of Course | English |
| Course Level | Third Cycle (Doctorate Degree) |
| Course Type | Elective |
| Course Coordinator | Prof.Dr. Selim AKYOKUŞ |
| Name of Lecturer(s) | Prof.Dr. Selim AKYOKUŞ |
| Assistant(s) | |
| Aim | This course will cover basics of NLP and applications of deep learning in natural language processing. Prerequisite for this class is Machine Learning. |
| Course Content | This course contains; Introduction,A simple NLP pipeline with scikit-learn,Word vectors,Recurrent Neural Networks,Language models,Pytorch and tensorflow,Text classification, text summarization, question answering,Exam Week study,Machine translation,Transformers,Lightweight AI,NLP systems in production,Project presentations,Project presentations. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Implement advanced neural network architectures using tensorflow or pytorch. | 2 | E |
| Complete a full NLP project involving advanced concepts in machine learning | 16, 2 | D, F |
| Describe various NLP algorithms such as those used for text classification and text generation | 12, 14, 21, 6, 9 | A, D, G |
| Teaching Methods: | 12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 2: Project Based Learning Model, 21: Simulation Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, D: Oral Exam, E: Homework, F: Project Task, G: Quiz |
Course Outline
| Order | Subjects | Preliminary Work |
|---|
| 1 | Introduction | |
| 2 | A simple NLP pipeline with scikit-learn | |
| 3 | Word vectors | |
| 4 | Recurrent Neural Networks | |
| 5 | Language models | |
| 6 | Pytorch and tensorflow | |
| 7 | Text classification, text summarization, question answering | |
| 8 | Exam Week study | |
| 9 | Machine translation | |
| 10 | Transformers | |
| 11 | Lightweight AI | |
| 12 | NLP systems in production | |
| 13 | Project presentations | |
| 14 | Project presentations | |
| Resources |
| Speech and Language Processing, Jurafsky and Martin, 3rd edition draft at https://web.stanford.edu/~jurafsky/slp3/ |
| Natural Language Processing with Python, Steven Bird, Ewan Klein, and Edward Loper at http://www.nltk.org/book/ |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications |
| No | Program Qualification | Contribution Level |
| 1 | 2 | 3 | 4 | 5 |
| 1 | Develop and deepen the current and advanced knowledge in the field with original thought and/or research and come up with innovative definitions based on Master's degree qualifications. | | X | | | |
| 2 | Conceive the interdisciplinary interaction which the field is related with ; come up with original solutions by using knowledge requiring proficiency on analysis, synthesis and assessment of new and complex ideas. | | | X | | |
| 3 | Evaluate and use new information within the field in a systematic approach and gain advanced level skills in the use of research methods in the field. | | | X | | |
| 4 | Develop an innovative knowledge, method, design and/or practice or adapt an already known knowledge, method, design and/or practice to another field. | | | | | |
| 5 | Broaden the borders of the knowledge in the field by producing or interpreting an original work or publishing at least one scientific paper in the field in national and/or international refereed journals. | | | | X | |
| 6 | Contribute to the transition of the community to an information society and its sustainability process by introducing scientific, technological, social or cultural improvements. | | | | | |
| 7 | Independently perceive, design, apply, finalize and conduct a novel research process. | | | | X | |
| 8 | Ability to communicate and discuss orally, in written and visually with peers by using a foreign language at least at a level of European Language Portfolio C1 General Level. | | | | X | |
| 9 | Critical analysis, synthesis and evaluation of new and complex ideas in the field. | | X | | | |
| 10 | Recognizes the scientific, technological, social or cultural improvements of the field and contribute to the solution finding process regarding social, scientific, cultural and ethical problems in the field and support the development of these values. | | | X | | |
Assessment Methods
| Contribution Level | Absolute Evaluation |
| Rate of Midterm Exam to Success | | 50 |
| Rate of Final Exam to Success | | 50 |
| Total | | 100 |
| ECTS / Workload Table |
| Activities | Number of | Duration(Hour) | Total Workload(Hour) |
| Course Hours | 14 | 3 | 42 |
| Guided Problem Solving | 0 | 0 | 0 |
| Resolution of Homework Problems and Submission as a Report | 10 | 2 | 20 |
| Term Project | 0 | 0 | 0 |
| Presentation of Project / Seminar | 8 | 10 | 80 |
| Quiz | 6 | 3 | 18 |
| Midterm Exam | 1 | 30 | 30 |
| General Exam | 1 | 50 | 50 |
| Performance Task, Maintenance Plan | 0 | 0 | 0 |
| Total Workload(Hour) | 240 |
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(240/30) | 8 |
| ECTS of the course: 30 hours of work is counted as 1 ECTS credit. |
Detail Informations of the Course
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|
| NATURAL LANGUAGE PROCESSING | COED1112914 | Fall Semester | 3+0 | 3 | 8 |
| Prerequisites Courses | |
| Recommended Elective Courses | |
| Language of Course | English |
| Course Level | Third Cycle (Doctorate Degree) |
| Course Type | Elective |
| Course Coordinator | Prof.Dr. Selim AKYOKUŞ |
| Name of Lecturer(s) | Prof.Dr. Selim AKYOKUŞ |
| Assistant(s) | |
| Aim | This course will cover basics of NLP and applications of deep learning in natural language processing. Prerequisite for this class is Machine Learning. |
| Course Content | This course contains; Introduction,A simple NLP pipeline with scikit-learn,Word vectors,Recurrent Neural Networks,Language models,Pytorch and tensorflow,Text classification, text summarization, question answering,Exam Week study,Machine translation,Transformers,Lightweight AI,NLP systems in production,Project presentations,Project presentations. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Implement advanced neural network architectures using tensorflow or pytorch. | 2 | E |
| Complete a full NLP project involving advanced concepts in machine learning | 16, 2 | D, F |
| Describe various NLP algorithms such as those used for text classification and text generation | 12, 14, 21, 6, 9 | A, D, G |
| Teaching Methods: | 12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 2: Project Based Learning Model, 21: Simulation Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, D: Oral Exam, E: Homework, F: Project Task, G: Quiz |
Course Outline
| Order | Subjects | Preliminary Work |
|---|
| 1 | Introduction | |
| 2 | A simple NLP pipeline with scikit-learn | |
| 3 | Word vectors | |
| 4 | Recurrent Neural Networks | |
| 5 | Language models | |
| 6 | Pytorch and tensorflow | |
| 7 | Text classification, text summarization, question answering | |
| 8 | Exam Week study | |
| 9 | Machine translation | |
| 10 | Transformers | |
| 11 | Lightweight AI | |
| 12 | NLP systems in production | |
| 13 | Project presentations | |
| 14 | Project presentations | |
| Resources |
| Speech and Language Processing, Jurafsky and Martin, 3rd edition draft at https://web.stanford.edu/~jurafsky/slp3/ |
| Natural Language Processing with Python, Steven Bird, Ewan Klein, and Edward Loper at http://www.nltk.org/book/ |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications |
| No | Program Qualification | Contribution Level |
| 1 | 2 | 3 | 4 | 5 |
| 1 | Develop and deepen the current and advanced knowledge in the field with original thought and/or research and come up with innovative definitions based on Master's degree qualifications. | | X | | | |
| 2 | Conceive the interdisciplinary interaction which the field is related with ; come up with original solutions by using knowledge requiring proficiency on analysis, synthesis and assessment of new and complex ideas. | | | X | | |
| 3 | Evaluate and use new information within the field in a systematic approach and gain advanced level skills in the use of research methods in the field. | | | X | | |
| 4 | Develop an innovative knowledge, method, design and/or practice or adapt an already known knowledge, method, design and/or practice to another field. | | | | | |
| 5 | Broaden the borders of the knowledge in the field by producing or interpreting an original work or publishing at least one scientific paper in the field in national and/or international refereed journals. | | | | X | |
| 6 | Contribute to the transition of the community to an information society and its sustainability process by introducing scientific, technological, social or cultural improvements. | | | | | |
| 7 | Independently perceive, design, apply, finalize and conduct a novel research process. | | | | X | |
| 8 | Ability to communicate and discuss orally, in written and visually with peers by using a foreign language at least at a level of European Language Portfolio C1 General Level. | | | | X | |
| 9 | Critical analysis, synthesis and evaluation of new and complex ideas in the field. | | X | | | |
| 10 | Recognizes the scientific, technological, social or cultural improvements of the field and contribute to the solution finding process regarding social, scientific, cultural and ethical problems in the field and support the development of these values. | | | X | | |
Assessment Methods
| Contribution Level | Absolute Evaluation |
| Rate of Midterm Exam to Success | | 50 |
| Rate of Final Exam to Success | | 50 |
| Total | | 100 |
Numerical Data
Ekleme Tarihi: 24/12/2023 - 02:34Son Güncelleme Tarihi: 24/12/2023 - 02:34
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