Mekatronik ve Robotik Mühendisliği (İngilizce) (Tezli) | |||||
Yüksek Lisans | Programın Süresi: 2 | Kredi Sayısı: 120 | TYYÇ: 7. Düzey | QF-EHEA: 2. Düzey | EQF: 7. Düzey |
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 | Bahar | ||||
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 |
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N None | S Supportive | H Highly Related |
Program Outcomes and Competences | Level | Assessed by | |
1) | N | ||
2) | N | ||
3) | N | ||
4) | N | ||
5) | N | ||
6) | N | ||
7) | N | ||
8) | N | ||
9) | N | ||
10) | N |
Prepared by and Date | ŞENİZ DEMİR , February 2024 |
Course Coordinator | ŞENİZ DEMİR |
Semester | Bahar |
Name of Instructor | Doç. Dr. ŞENİZ DEMİR |
Hafta | Konu |
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 |
Activity | No/Weeks | Hours | Calculation | ||||
No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
Ders Saati | 14 | 2 | 3 | 2 | 98 | ||
Sunum / Seminer | 1 | 30 | 1 | 31 | |||
Rapor Teslimi | 1 | 30 | 1 | 31 | |||
Final | 1 | 25 | 2 | 1 | 28 | ||
Total Workload | 188 | ||||||
Total Workload/25 | 7.5 | ||||||
ECTS | 7.5 |