Data Science (Master) (with Thesis) | |||||
Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 |
Course Code: | VB5011 | ||||
Course Name: | Natural Language Processing | ||||
Semester: |
Fall Spring |
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Course Credits: |
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Language of instruction: | Turkish | ||||
Course Condition: | |||||
Does the Course Require Work Experience?: | No | ||||
Type of course: | Departmental Elective | ||||
Course Level: |
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Mode of Delivery: | E-Learning | ||||
Course Coordinator: | Doç. Dr. ŞEBNEM ÖZDEMİR | ||||
Course Lecturer(s): |
Öğr. Gör. STAFF 1 |
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Course Assistants: |
Course Objectives: | The main objective of this course is to introduce students to the basic concepts and theoretical knowledge of Natural Language Processing (NLP) and the methods and technologies commonly used in this field. Within the scope of the course, it will be taught how algorithms and models are developed that enable computers to interact with human language and how these models are integrated with real-world data. By examining how deep learning techniques are applied to NLP, it is aimed to increase the capacity of students to work on current problems in the field and to produce new solutions. |
Course Content: | 1. Fundamentals and Application Areas of Natural Language Processing 2. Text Preprocessing and Cleaning Methods 3. Algorithms and Models for Natural Language Processing 4. Deep Learning and Natural Language Processing 5. Current Developments in Natural Language Processing and Application Studies |
The students who have succeeded in this course;
1) To be able to define the introduction and basic concepts of natural language processing 2) To be able to apply text pre-processing and cleaning techniques 3) Understand and use general purpose natural language processing algorithms 4) Integrate deep learning-based natural language processing models 5) To be able to develop solution approaches to current NLP problems |
Week | Subject | Related Preparation |
1) | History and Development of Natural Language Processing | |
2) | Natural Language Processing and Linguistic Foundations | |
3) | Text Preprocessing: Tokenization, Stemming and Lemmatization | |
4) | Word Type Tagging and Syntactic Analysis | |
5) | Semantics, Inference and Signification | |
6) | Algorithms: TF-IDF, N-gram Models and Sentiment Analysis | |
7) | Deep Learning and NLP: Embeddings, RNN and LSTM | |
8) | Midterm Exam | |
9) | Attention Mechanisms and Transformer Models | |
10) | Review of Language Models and Pre-Trained Models (BERT, GPT) | |
11) | Machine Translation and Sequence-to-Sequence Models | |
12) | Speech Recognition and Transcription Systems | |
13) | Chatbots and Analyzing the Meaning of Dialog | |
14) | Recommender Systems and Personalized Applications | |
15) | Ethics and Security in Natural Language Processing | |
16) | final exam |
Course Notes / Textbooks: | Herhangi bir ders kitabı bulunmamaktadır. There is no textbook. |
References: | Güncel makaleler, kitaplar kullanılacaktır. Current articles and books will be used. |
Course Learning Outcomes | 1 |
2 |
3 |
4 |
5 |
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Program Outcomes | |||||
1) Students who successfully complete this program, Knows the scope of technical applications of data science and the tools that can be used. | 2 | 3 | 3 | 2 | 3 |
2) Students who successfully complete this program, Knows the effects of application results on society-culture-law. | 2 | 3 | 3 | 2 | 2 |
3) Students who complete this program; Recognizes mathematics and code in application processes. | 2 | 2 | 2 | 2 | 3 |
4) Students who complete this program; Explain the effects of processes in data science on output and individual. | 3 | 3 | 2 | 3 | 2 |
5) Students who successfully complete this program, Understands the insight-foresight and foresight created by data science as a whole in the face of a certain discipline/case. | 3 | 2 | 3 | 2 | 3 |
No Effect | 1 Lowest | 2 Average | 3 Highest |
Program Outcomes | Level of Contribution | |
1) | Students who successfully complete this program, Knows the scope of technical applications of data science and the tools that can be used. | 3 |
2) | Students who successfully complete this program, Knows the effects of application results on society-culture-law. | 2 |
3) | Students who complete this program; Recognizes mathematics and code in application processes. | 2 |
4) | Students who complete this program; Explain the effects of processes in data science on output and individual. | 3 |
5) | Students who successfully complete this program, Understands the insight-foresight and foresight created by data science as a whole in the face of a certain discipline/case. | 2 |
Semester Requirements | Number of Activities | Level of Contribution |
Midterms | 1 | % 40 |
Final | 1 | % 60 |
total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 40 | |
PERCENTAGE OF FINAL WORK | % 60 | |
total | % 100 |
Activities | Number of Activities | Workload |
Course Hours | 15 | 61 |
Midterms | 8 | 29 |
Final | 8 | 69 |
Total Workload | 159 |