Information Package / Course Catalogue
| Mechatronics and Robotics Engineering (English) (Thesis) | |||||
| Master | Length of the Programme: 2 | Number of Credits: 120 | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF: Level 7 |
Ders Genel Tanıtım Bilgileri
| School/Faculty/Institute | Graduate School | ||||
| Course Code | ITC 533 | ||||
| Course Title in English | Natural Language Processing | ||||
| Course Title in Turkish | Natural Language Processing | ||||
| Language of Instruction | EN | ||||
| Type of Course | Flipped Classroom | ||||
| Level of Course | Advanced | ||||
| Semester | Spring | ||||
| Contact Hours per Week |
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| Estimated Student Workload | 188 hours per semester | ||||
| Number of Credits | 7.5 ECTS | ||||
| Grading Mode | Standard Letter Grade | ||||
| Pre-requisites | None | ||||
| Expected Prior Knowledge | None | ||||
| Co-requisites | None | ||||
| Registration Restrictions | Only Graduate Students | ||||
| Overall Educational Objective | To learn the fundamentals of natural language processing, gain insights into open research problems in natural language, and design/implement language tools to solve well-known research problems. | ||||
| Course Description | This course covers the fundamentals of natural language processing and techniques for processing and making sense of text data written in natural language. Topics include part of speech tagging, parsing, word sense disambiguation, and question answering. | ||||
| Course Description in Turkish | This course covers the fundamentals of natural language processing and techniques for processing and making sense of text data written in natural language. Topics include part of speech tagging, parsing, word sense disambiguation, and question answering. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) know basic principles, algorithms and theoretical issues underlying natural language processing 2) assess the performance of computational techniques and tools to process text written in human language 3) design and implement a language processing tool for a research problem |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 |
|---|---|---|---|
| 1) An ability to develop and deepen one's knowledge in the field of mechatronics and robotics engineering at the level of expertise based on acquired undergraduate level qualifications. | |||
| 2) An ability to acquire scientific and practical knowledge in mechatronics and robotics. | |||
| 3) A comprehensive knowledge about analysis and modeling methods in mechatronics and their limitations. | |||
| 4) An ability to design and apply analytical, modeling and experimental based research by analyzing and interpreting complex situations encountered in the design process. | |||
| 5) An ability to transmit the process and results of the work of mechatronics and robotics systems systematically and clearly in written and oral form in national and international environments. | |||
| 6) An ability to recognize social, scientific and ethical values in the stages of designing and realizing mechatronics and robotic systems and in all professional activities. | |||
| 7) An ability to follow new and developing practices in the profession and to apply them in their work. | |||
| 8) An ability to take leadership in multi-disciplinary teams, taking responsibility in the design and analysis of mechatronics and robotic systems in complex situations. | |||
| 9) An ability to communicate verbally and in writing in English at least at the level of B2 of European Language Portfolio. | |||
| 10) An understanding of the social and environmental aspects of mechatronics and robotics applications. |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | An ability to develop and deepen one's knowledge in the field of mechatronics and robotics engineering at the level of expertise based on acquired undergraduate level qualifications. | N | |
| 2) | An ability to acquire scientific and practical knowledge in mechatronics and robotics. | N | |
| 3) | A comprehensive knowledge about analysis and modeling methods in mechatronics and their limitations. | N | |
| 4) | An ability to design and apply analytical, modeling and experimental based research by analyzing and interpreting complex situations encountered in the design process. | N | |
| 5) | An ability to transmit the process and results of the work of mechatronics and robotics systems systematically and clearly in written and oral form in national and international environments. | N | |
| 6) | An ability to recognize social, scientific and ethical values in the stages of designing and realizing mechatronics and robotic systems and in all professional activities. | N | |
| 7) | An ability to follow new and developing practices in the profession and to apply them in their work. | N | |
| 8) | An ability to take leadership in multi-disciplinary teams, taking responsibility in the design and analysis of mechatronics and robotic systems in complex situations. | N | |
| 9) | An ability to communicate verbally and in writing in English at least at the level of B2 of European Language Portfolio. | N | |
| 10) | An understanding of the social and environmental aspects of mechatronics and robotics applications. | N |
| Prepared by and Date | ŞENİZ DEMİR , February 2024 |
| Course Coordinator | ŞENİZ DEMİR |
| Semester | Spring |
| Name of Instructor | Assoc. Prof. Dr. ŞENİZ DEMİR |
Course Contents
| Week | Subject |
| 1) | Introduction |
| 2) | Language Models |
| 3) | Word Classes, Part-of-Speech Tagging, and HMMs |
| 3) | Word Classes, Part-of-Speech Tagging, and HMMs |
| 4) | Word Classes, Part-of-Speech Tagging, and HMMs |
| 5) | Grammars, Treebanks, Dependency Parsing |
| 6) | Grammars, Treebanks, Dependency Parsing |
| 7) | Research Report Presentations |
| 8) | Introduction to Deep Learning |
| 9) | Word Embedding |
| 10) | Recurrent Neural Networks |
| 11) | Recurrent Neural Networks |
| 12) | Encoder-Decoder Models and Attention |
| 13) | Encoder-Decoder Models and Attention |
| 14) | Term Project Presentations |
| 15) | Final Exam/Project/Presentation Period |
| 16) | Final Exam/Project/Presentation Period |
| Required/Recommended Readings | - Speech and Language Processing, D. Jurafsky, J.H. Martin, 2nd Edition, Pearson-Prentice Hall, 2009. • (Supplementary) Foundations of Statistical Natural Language Processing, C.D. Manning,H. Schütze, MIT Press, 2002. | |||||||||||||||
| Teaching Methods | Flipped Classroom | |||||||||||||||
| Homework and Projects | Research Report and Term Project | |||||||||||||||
| Laboratory Work | None | |||||||||||||||
| Computer Use | Required | |||||||||||||||
| Other Activities | None | |||||||||||||||
| Assessment Methods |
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| Course Administration |
demirse@mef.edu.tr 536 Instructor’s office: 5th floor – Room 536 Office hours: Via appointment E-mail address: demirse@mef.edu.tr Rules for attendance: No attendance required Statement on plagiarism: YÖK Regulations |
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ECTS Student Workload Estimation
| Activity | No/Weeks | Hours | Calculation | ||||
| No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
| Course Hours | 14 | 2 | 3 | 2 | 98 | ||
| Presentations / Seminar | 1 | 30 | 1 | 31 | |||
| Paper Submission | 1 | 30 | 1 | 31 | |||
| Final Examination | 1 | 25 | 2 | 1 | 28 | ||
| Total Workload | 188 | ||||||
| Total Workload/25 | 7.5 | ||||||
| ECTS | 7.5 | ||||||