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 Computer And Information Technologies
  • Artificial Intelligence And Machine Learning Pr. (English)
  • Course Structure Diagram with Credits
  • Natural Language Processing
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications
  • ECTS Credit Load

Course Introduction Information
Code - Course Title YZM4004 - Natural Language Processing
Course Type Required Courses
Language of Instruction İngilizce
Laboratory + Practice 3+0
ECTS 6.0
Course Instructor(s) DOKTOR ÖĞRETİM ÜYESİ NİHAL İNCE
Mode of Delivery This course is offered exclusively in an in-person format.
Prerequisites There are no prerequisites or co-requisites for this course.
Courses Recomended There are no other recommended courses related to this one.
Required or Recommended Resources Jurafsky, D., & Martin, J. H. (2024). Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition (3rd ed. draft)Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python: Analyzing text with the natural language toolkit. O'Reilly Media.Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval. Cambridge University Press.Tunstall, L., von Werra, L., & Wolf, T. (2022). Natural language processing with transformers: Building language applications with Hugging Face. O'Reilly Media.
Recommended Reading List Jurafsky, D., & Martin, J. H. (2024). Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition (3rd ed. draft)Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python: Analyzing text with the natural language toolkit. O'Reilly Media.Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval. Cambridge University Press.Tunstall, L., von Werra, L., & Wolf, T. (2022). Natural language processing with transformers: Building language applications with Hugging Face. O'Reilly Media.
Assessment methods and criteria 1 midterm exam, 1 project, 1 final exam
Work Placement This is not suitable for this class.
Sustainability Development Goals

Content

Weeks Topics
Week - 1 Intro to NLP: Challenges. Text Preprocessing: Tokenization, Stemming, Lemmatization. NLTK & spaCy.
Week - 2 Classical NLP: N-gram Language Models.
Week - 3 Classical NLP: TF-IDF and Text Classification with Naive Bayes.
Week - 4 Word Embeddings: Vector Representation of Words. The Word2Vec Architecture (CBOW, Skip-gram).
Week - 5 Using Word2Vec and GloVe with the Gensim library.
Week - 6 Deep Learning for NLP: Text Classification with RNN/LSTM/GRU.
Week - 7 The Sequence-to-Sequence (Seq2Seq) Architecture.
Week - 8 The Attention Mechanism and Transformers.
Week - 9 Modern NLP: Introduction to the Hugging Face Ecosystem (pipeline, AutoModel).
Week - 10 The BERT Architecture and Fine-Tuning for Text Classification.
Week - 11 Named Entity Recognition (NER).
Week - 12 Question-Answering and Summarization (Hands-on).
Week - 13 Ethics in NLP: Generative AI (GPT) and Bias.
Week - 14 Introduction to Speech-to-Text or Machine Translation (NMT) Concepts.

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Discussion
  • Question & Answer
  • Team/Group Work
  • Drill - Practise
  • Problem Solving
  • Report Preparation and/or Presentation
  • Proje Design/Management
  • Competences
  • Productive
  • Rational
  • Questoning
  • Work in teams
  • Use time effectively
  • Eleştirel düşünebilme
  • Abstract analysis and synthesis
  • Problem solving
  • Concern for quality
  • Information Management
  • Elementary computing skills
  • Decision making
  • Project Design and Management

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
Toplam (%) 0
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