| Course Objectives: |
Natural language processing (NLP) is a very important technology in the information age. Exciting advances in the field of natural language processing (NLP) have emerged recently, enabling systems that can perform tasks such as text translation, question answering, and spoken conversations. This course aims to provide students with a basic understanding of NLP, including standard frameworks, algorithms and techniques used to solve a variety of NLP problems. The curriculum will cover topics such as language modelling, representation learning, text classification, string tagging, syntactic parsing, machine translation and question answering, with a particular focus on recent deep learning approaches. Through this course, students will receive a comprehensive introduction to NLP concepts, methods, algorithms, applications, and cutting-edge method research in the field of deep learning for NLP. |
| Course Content: |
This lesson; Introduction to Natural Language Processing (NLP), Linguistic Fundamentals, Regular Exp., Text Normalization, Edit Distance, N-gram Models, Machine Learning Fundamentals, Text Classification, Naive Bayes and Logistic Regression, Vector Semantics and Dense Word Embeddings, Artificial Neural Networks and Neural Language Models, Sequence Tagging for Parts of Speech and Named Entities, Exam Week, RNNs and LSTMs, Transformers and Pre-Trained Language Models, Fine-Tuned and Masked Language Models, Machine Translation, Question Answering and Information Retrieval, Chatbots and Conversational Systems, Automated Speech Recognition and Text-to-Speech Conversion, Context-Free Grammars, Formation Parsing, Dependency Parsing, Logical Representations of Sentence Meaning, Topic Review and Project Presentations; Includes topics. |
| Week |
Subject |
Related Preparation |
| 1) |
Introduction to Natural Language Processing |
Textbook |
| 2) |
Linguistic Fundamentals, Regular Exp., Text Normalization, Edit Distance |
Textbook |
| 3) |
N-gram Models |
Textbook |
| 4) |
Machine Learning Fundamentals, Text Classification, Naive Bayes and Logistic Regression |
Textbook |
| 5) |
Vector Semantics and Dense Word Embeddings |
Textbook |
| 6) |
Artificial Neural Networks and Neural Language Models |
Textbook |
| 7) |
Sequence Tagging for Parts of Speech and Named Entities |
Textbook |
| 8) |
Midterm |
Textbook |
| 9) |
RNNs and LSTMs |
Textbook |
| 10) |
Transformers and Pretrained Language Models, Fine-Tuned and Masked Language Models |
Textbook |
| 11) |
Machine Translation, Question Answering and Information Gathering |
Textbook |
| 12) |
Chatbots and Conversational Systems, Automatic Speech Recognition and Text-to-Speech Conversion |
Textbook |
| 13) |
Context-Free Grammars, Formation Parsing, Dependency Parsing, Logical Representations of Sentence Meaning |
Textbook |
| 14) |
Revision |
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Program Outcomes |
Level of Contribution |
| 1) |
Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in complex engineering problems. |
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| 2) |
Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. |
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| 3) |
Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose |
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| 4) |
Ability to devise, select, and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively. |
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| 5) |
Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions. |
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| 6) |
Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. |
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| 7) |
Ability to communicate effectively in Turkish, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions. |
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| 8) |
Knowledge of the global and societal impacts of engineering practices on priority issues such as health, environment and safety and contemporary issues; knowledge of the legal aspects of engineering solutions. awareness of the consequences |
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| 9) |
Consciousness to behave according to ethical principles and professional and ethical responsibility; knowledge on standards used in engineering practice. |
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| 10) |
Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and knowledge about sustainable development. |
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| 11) |
Ability to design systems to meet desired needs |
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| 12) |
Ability to apply basic sciences in the field of computer engineering |
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| 13) |
Ability to implement designs by experiments |
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| 14) |
Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself. |
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Istanbul Health and Technology University INFORMATION PACKAGE / COURSE CATALOGUE