| 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 |
|
| |
Program Outcomes |
Level of Contribution |
| 1) |
Sufficient knowledge in mathematics, science and software engineering discipline-specific topics; the theoretical and practical knowledge in these areas, the ability to use in complex engineering problems. |
3 |
| 2) |
The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modelling methods for this purpose. |
3 |
| 3) |
The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. |
3 |
| 4) |
Ability to develop, select and use modern techniques and tools necessary for analysis and solution of complex problems in engineering applications; ability to use information technologies effectively. |
3 |
| 5) |
Ability to design experiments, conduct experiments, collect data, analyse and interpret the results of complex engineering problems or discipline-specific research topics. |
3 |
| 6) |
Disiplin içi ve çok disiplinli takımlarda etkin biçimde çalışabilme becerisi; bireysel çalışma becerisi. |
3 |
| 7) |
Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal. |
3 |
| 8) |
Effective communication skills in Turkish oral and written communication; at least one foreign language knowledge; ability to write effective reports and understand written reports, to prepare design and production reports, to make effective presentations, to give clear and understandable instructions and to receive. |
2 |
| 9) |
Conformity to ethical principles, professional and ethical responsibility; Information on standards used in engineering applications. |
2 |
| 10) |
Information on practices in business, such as project management, risk management and change management; awareness about entrepreneurship, innovation; information on sustainable development. |
2 |
| 11) |
Information on the effects of engineering applications on health, environment, and safety in universal and social dimensions, and on the problems of the modern age in engineering; awareness of the legal consequences of engineering solutions. |
2 |
| 12) |
Adequate skills in the analysis, design, verification, evaluation, implementation, implementation, and maintenance of software systems |
2 |
Istanbul Health and Technology University INFORMATION PACKAGE / COURSE CATALOGUE