Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|
| ADVANCED TOPICS in NATURAL LANGUAGE PROCESSING | COED1213993 | Spring 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. Reda ALHAJJ |
| Name of Lecturer(s) | Prof.Dr. Reda ALHAJJ |
| Assistant(s) | |
| Aim | The objective of this course is to explore recent research areas within natural language processing with sufficient depth. By the end of the course, participants will be equipped to actively contribute to research within their chosen subjects. This course is aimed for graduate students in computer science/engineering. The course assumes that students have a foundational knowledge of machine learning and prior experience or coursework in natural language processing. Topics covered encompass natural language understanding, representation learning, contextual representations, multitask learning, learning from multiple modalities, deep generative models, reinforcement learning, generative adversarial learning, NLP methods and metrics. The specific list of topics for the current year will be dependent on the instructor and prevailing trends in natural language processing research, with details announced during the course. |
| Course Content | This course contains; Natural language understanding,Representation learning,Contextual representation models,Semantic and syntactic parsing,Question answering,Machine translation,Exam week,Multitask learning, Learning from multiple modalities,Language generation,Deep generative models,Large language models,Reinforcement learning,Generative adversarial learning
,Project/research presentations. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| 1 - Acquire knowledge about the selected advanced topics in natural language processing with a focus on design of learning algorithms and evaluation of learning algorithms | 2 | E |
| 2 - Develop the ability to read and understand recent scientific literature in the field of natural language processing, apply the knowledge obtained by reading scientific papers, discuss and compare methods and assess their potentials and shortcomings | 16, 2 | D, F |
| 3 - Gain a comprehensive understanding of advanced methods, and apply this knowledge to solutions of practical problems | 12, 14, 21, 6, 9 | A, D, G |
| 4 - Carry out research projects in a chosen area of interest within natural language processing. | 14, 2 | F |
| 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 | Natural language understanding | |
| 2 | Representation learning | |
| 3 | Contextual representation models | |
| 4 | Semantic and syntactic parsing | |
| 5 | Question answering | |
| 6 | Machine translation | |
| 7 | Exam week | |
| 8 | Multitask learning, Learning from multiple modalities | |
| 9 | Language generation | |
| 10 | Deep generative models | |
| 11 | Large language models | |
| 12 | Reinforcement learning | |
| 13 | Generative adversarial learning
| |
| 14 | Project/research presentations | |
| Resources |
| - Eisenstein (2019), Introduction to Natural Language Processing.
- Jurafsky and Martin (~2021), Speech and Language Processing.
- Manning and Schütze, Foundations of Statistical NLP.
- Murphy, Machine Learning: a Probabilistic Perspective
- Goodfellow, Bengio and Courville (2016), Deep Learning.
- Bird et al, NLP with Python, a.k.a. the NLTK book.
- Lewis Tunstall, Leandro von Werra, and Thomas Wolf. Natural Language Processing with Transformers
- Selected Papers.
|
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 |
|---|
| ADVANCED TOPICS in NATURAL LANGUAGE PROCESSING | COED1213993 | Spring 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. Reda ALHAJJ |
| Name of Lecturer(s) | Prof.Dr. Reda ALHAJJ |
| Assistant(s) | |
| Aim | The objective of this course is to explore recent research areas within natural language processing with sufficient depth. By the end of the course, participants will be equipped to actively contribute to research within their chosen subjects. This course is aimed for graduate students in computer science/engineering. The course assumes that students have a foundational knowledge of machine learning and prior experience or coursework in natural language processing. Topics covered encompass natural language understanding, representation learning, contextual representations, multitask learning, learning from multiple modalities, deep generative models, reinforcement learning, generative adversarial learning, NLP methods and metrics. The specific list of topics for the current year will be dependent on the instructor and prevailing trends in natural language processing research, with details announced during the course. |
| Course Content | This course contains; Natural language understanding,Representation learning,Contextual representation models,Semantic and syntactic parsing,Question answering,Machine translation,Exam week,Multitask learning, Learning from multiple modalities,Language generation,Deep generative models,Large language models,Reinforcement learning,Generative adversarial learning
,Project/research presentations. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| 1 - Acquire knowledge about the selected advanced topics in natural language processing with a focus on design of learning algorithms and evaluation of learning algorithms | 2 | E |
| 2 - Develop the ability to read and understand recent scientific literature in the field of natural language processing, apply the knowledge obtained by reading scientific papers, discuss and compare methods and assess their potentials and shortcomings | 16, 2 | D, F |
| 3 - Gain a comprehensive understanding of advanced methods, and apply this knowledge to solutions of practical problems | 12, 14, 21, 6, 9 | A, D, G |
| 4 - Carry out research projects in a chosen area of interest within natural language processing. | 14, 2 | F |
| 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 | Natural language understanding | |
| 2 | Representation learning | |
| 3 | Contextual representation models | |
| 4 | Semantic and syntactic parsing | |
| 5 | Question answering | |
| 6 | Machine translation | |
| 7 | Exam week | |
| 8 | Multitask learning, Learning from multiple modalities | |
| 9 | Language generation | |
| 10 | Deep generative models | |
| 11 | Large language models | |
| 12 | Reinforcement learning | |
| 13 | Generative adversarial learning
| |
| 14 | Project/research presentations | |
| Resources |
| - Eisenstein (2019), Introduction to Natural Language Processing.
- Jurafsky and Martin (~2021), Speech and Language Processing.
- Manning and Schütze, Foundations of Statistical NLP.
- Murphy, Machine Learning: a Probabilistic Perspective
- Goodfellow, Bengio and Courville (2016), Deep Learning.
- Bird et al, NLP with Python, a.k.a. the NLTK book.
- Lewis Tunstall, Leandro von Werra, and Thomas Wolf. Natural Language Processing with Transformers
- Selected Papers.
|
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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