Course Details

 

Course Details Language of Instruction English Level of Course Unit Doctorate's Degree Department / Program ELECTRICAL AND COMPUTER ENGINEERING Type of Program Formal Education Type of Course Unit Elective Course Delivery Method Face To Face Objectives of the Course The objective of this course is to explain the fundamental and advanced principles of NLP and modern language modeling, while analyzing and evaluating NLP algorithms using quantitative metrics and benchmark datasets. Furthermore, it aims to enable students to design advanced NLP systems using deep learning and transformer architectures and employ these NLP techniques to solve real-world problems across healthcare, engineering, and computational sciences. Course Content Advanced Natural Language Processing and Applications is an advanced PhD-level course focusing on modern NLP methods, including statistical foundations, machine learning-based NLP, deep learning architectures, and cutting-edge transformer models. The course covers distributed word representations, sequence modeling, attention mechanisms, transformer encoders and decoders, large language models (LLMs), prompt engineering, and evaluation methodologies. Applications include NLP in different domains, information extraction, text mining, dialogue systems, and generative Al. Course Methods and Techniques The course utilizes a combination of theoretical instruction and practical application. Pedagogical methods include case studies and research project presentations , alongside programming assignments and research projects designed to help students translate theory into practice. Technical methodologies covered include machine learning for NLP (features, classifiers, embeddings) , deep learning models (RNNs, CNNs, LSTM, seq2seq, etc.) , transformer architectures, pre-trained language models, and generative AI. Prerequisites and co-requisities None Course Coordinator Asist Prof.Dr. Mehmet Gökhan Bakal https://avesis.agu.edu.tr/gokhan.bakal gokhan.bakal@agu.edu.tr Name of Lecturers Asist Prof.Dr. Mehmet Gökhan Bakal https://avesis.agu.edu.tr/gokhan.bakal gokhan.bakal@agu.edu.tr Assistants None Work Placement(s) No Recommended or Required Reading Resources Course Notes Core Textbooks: Jurafsky, D., & Martin, J. H. (Current Draft / 3rd ed.). Speech and Language Processing. (Primary reference for foundational concepts and statistical NLP). Tunstall, L., von Werra, L., & Wolf, T. (2022). Natural Language Processing with Transformers: Building Language Applications with Hugging Face. O'Reilly Media. (Essential for practical implementation of transformers and LLMs). Goldberg, Y. (2017). Neural Network Methods for Natural Language Processing. Morgan & Claypool. (For the theoretical foundations of deep learning-based NLP models). Articles and Supplementary Readings: Seminal research papers that form the basis of modern language models (e.g., Vaswani et al., 2017, "Attention Is All You Need", and foundational publications on the BERT/GPT model families). Recent state-of-the-art literature on generative AI and information extraction selected from top-tier NLP venues such as ACL, EMNLP, and NAACL. Domain-specific case studies and research articles focusing on the application of NLP in real-world scenarios across healthcare, engineering, and computational sciences (e.g., biomedical language models). Course Category Mathematics and Basic Sciences %30 Engineering %60 Science %10

 
 
 
 
  
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