Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Faculty of Engineering
  • Department of Computer Engineering (English)
  • Course Structure Diagram with Credits
  • Introduction to Natural Language Processing
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications

Course Introduction Information

Code - Course Title BİM463 - Introduction to Natural Language Processing
Course Type Area Elective Courses
Language of Instruction İngilizce
Laboratory + Practice 3+0
ECTS 4.5
Course Instructor(s) ARAŞTIRMA GÖREVLİSİ DOKTOR GÖKHAN GÖKSEL
Mode of Delivery This course is normally delivered face to face. However, in special circumstances such as pandemics and natural disasters, it is carried out in synchronous and/or asynchronous distance education format.
Prerequisites There is no prerequisite or co-requisite for this course.
Courses Recomended
Recommended Reading List
Assessment methods and criteria Homework, Quiz, Midterm Exam, Final Exam.
Work Placement
Sustainability Development Goals

Content

Weeks Topics
Week - 1 Regular Expressions and Text Processing
Week - 2 N-gram Language Models
Week - 3 Naive Bayes and Text Classification
Week - 4 Logistic regression
Week - 5 Vector Semantics and Embeddings
Week - 6 Neural Networks and Neural Language Models
Week - 7 Midterm
Week - 8 Sequence Tagging for Parts of Speech and Named Entities
Week - 9 RNNs and LSTMs
Week - 10 Transformers and Pre-Trained Language Models
Week - 11 Optimization and Masked Language Models
Week - 12 Machine Translation, Question Answering and Information Retrieval
Week - 13 Context-Free Languages, Dependency Parsing
Week - 14 Computational Semantics and Semantic Parsing

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Discussion
  • Question & Answer
  • Problem Solving
  • Competences
  • Effective use of a foreign language
  • Problem solving
  • To work autonomously

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 30
Quiz 1 30
Final Exam 1 40
Toplam (%) 100
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