AID405 Natural Language Processing
Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026
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Artificial Intelligence and Data Engineering
Course Objectives
By the end of this course, students will be able to: 1. Explain the recent developments and innovations in natural language processing and their applications in various domains. 2. Implement and evaluate different types of natural language processing models using Python and its libraries. 3. Apply natural language processing models to solve real-world problems of interest. 4. Explore and research advanced topics in natural language processing and present their findings.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
The required textbooks for this course are: [1] Vajjala, S., Gupta, A., Majumder, B., & Surana H. (2020). Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems. O’Reilly Media. [2] Jurafsky, D., & Martin J.H. (2019). Speech and Language Processing (3rd ed.). Prentice Hall.
Additional Literature
The recommended textbooks for this course are: [1] Manning C.D., & Schütze H. (1999). Foundations of Statistical Natural Language Processing. MIT Press. [2] Goldberg Y. (2017). Neural Network Methods for Natural Language Processing. Morgan & Claypool Publishers.Teaching Methods
There will be a 1 hour of theory and explaining the background of the topic
Then we will continue with the programming and practice
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction to Natural Language Processing | PNLPP: Ch. 1; SLP: Ch. 1 |
| 2 | Text Processing and Regular Expressions Assignment 1: Text Processing with NLTK | PNLPP: Ch. 2; SLP: Ch. 2 |
| 3 | Language Modeling and Text Classification | PNLPP: Ch. 3; SLP: Ch. 3 & 4 |
| 4 | Information Extraction and Named Entity Recognition Assignment 2: Information Extraction and Named Entity Recognition with spaCy | PNLPP: Ch. 4; SLP: Ch. 5 & 7 |
| 5 | Machine Translation and Sequence-to-Sequence Models | PNLPP: Ch. 5; SLP: Ch. 10 & 11 |
| 6 | Word Embeddings and Neural Language Models Assignment 3: Word Embeddings and Neural Language Models with PyTorch | PNLPP: Ch.6; SLP: Ch.6 |
| 7 | Midterm exam | |
| 8 | Transformers and Contextualized Embeddings | PNLPP: Ch.7; SLP: Ch.12 |
| 9 | Natural Language Generation and Text Summarization Assignment 4: Natural Language Generation with Hugging Face Transformers | PNLPP: Ch.8; SLP: Ch.23 |
| 10 | Dialogue Systems and Chatbots | PNLPP: Ch.9; SLP: Ch.24 |
| 11 | Advanced Topics in Natural Language Processing (I) - Self-Supervised Learning and Contrastive Learning Assignment 5: Self-Supervised Learning and Contrastive Learning with PyTorch Lightning | PNLPP: Ch.10 |
| 12 | Advanced Topics in Natural Language Processing (II) - Graph Neural Networks and Geometric Deep Learning | PNLPP: Ch.11 |
| 13 | Advanced Topics in Natural Language Processing (III) - Meta-Learning and Few-Shot Learning Assignment 6: Meta-Learning and Few-Shot Learning with Reptile | PNLPP: Ch.12 |
| 14 | Advanced Topics in Natural Language Processing (IV)- Large Language Models | Hands-on |
| 15 | Final Project Presentations and Course Summary and Feedback |
Course Schedule (All Sections)
Office Hours & Room
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes :
Midterm
AI: Not AllowedAlignment with Learning Outcomes :
Quizzes
AI: Not AllowedAlignment with Learning Outcomes :
Project and presentation
AI: Not AllowedAlignment with Learning Outcomes :
Assignments
AI: Not AllowedAlignment with Learning Outcomes :
IUS Grading System
| Grading Scale | IUS Grading System | IUS Coeff. | Letter (B&H) | Numerical (B&H) |
|---|---|---|---|---|
| 0 - 44 | F | 0 | F | 5 |
| 45 - 54 | E | 1 | ||
| 55 - 64 | C | 2 | E | 6 |
| 65 - 69 | C+ | 2.3 | D | 7 |
| 70 -74 | B- | 2.7 | ||
| 75 - 79 | B | 3 | C | 8 |
| 80 - 84 | B+ | 3.3 | ||
| 85 - 94 | A- | 3.7 | B | 9 |
| 95 - 100 | A | 4 | A | 10 |
Late Work Policy
Information about late submission policies will be shared during class and posted in this section. Please check back for official guidelines.
ECTS Credit Calculation
📚 Student Workload
This 6 ECTS credit course corresponds to 150 hours of total student workload, distributed as follows:
Lecture hours
42 hours ⏳ (14 week × 3 h)
Assignments
21 hours ⏳ (7 week × 3 h)
Active labs
28 hours ⏳ (14 week × 2 h)
Home study
14 hours ⏳ (14 week × 1 h)
In-term exam study
10 hours ⏳ (1 week × 10 h)
Final exam study
11 hours ⏳ (1 week × 11 h)
Term project/presentation
24 hours ⏳ (12 week × 2 h)
150 Total Workload Hours
6 ECTS Credits
Course Policies
Academic Integrity
All work submitted must be your own. Plagiarism, cheating, or any form of academic dishonesty will result in disciplinary action according to university policies. When in doubt about citation practices, consult the instructor.
Attendance Policy
Students are expected to adhere to the attendance requirements as outlined in the International University of Sarajevo Study Rules and Regulations. Excessive absences, whether excused or unexcused, may impact academic performance and eligibility for assessment. Mandatory sessions (e.g., labs, workshops) require attendance unless formally exempted. For detailed policies on absences, documentation, and penalties, please refer to the official university regulations.
Technology & AI Policy
Laptops/tablets may be used for note-taking only during lectures. Phones should be silenced and put away during all class sessions. Audio/video recording requires prior permission from the instructor.
Artificial Intelligence (AI) Usage: The use of AI tools (e.g., ChatGPT, Copilot, Gemini) varies by assessment component. Please refer to the AI usage indicator next to each assessment item in the Assessment Methods and Criteria section above. Submitting AI-generated content as your own work, where AI is not explicitly allowed, constitutes an academic integrity violation.
Communication Policy
All course-related communication should occur through official university channels (institutional email or SIS). Emails should include [AID405] in the subject line.
Academic Quality Assurance Policy
Course Academic Quality Assurance is achieved through Semester Student Survey. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.
Learning Tips
Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.
Complete assigned readings or prep materials before class. Take notes, highlight key ideas, and jot down questions. Aim to grasp core concepts and their applications—not just facts.
Use course frameworks or methodologies to analyze problems, case studies, or projects. Begin early to allow time for reflection and refinement. Seek feedback to improve your work.
Don’t hesitate to reach out when something is unclear. Use office hours, discussion boards, or peer networks to clarify concepts and stay on track.
Syllabus Last Updated on Mar 03, 2026 | International University of Sarajevo
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Referencing Curricula Print this page
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 30 | Not Allowed | ||
| Semester Evaluation Components | |||||
| Midterm | 1 | 25 | Not Allowed | ||
| Quizzes | 3 | 15 | Not Allowed | ||
| Project and presentation | 1 | 15 | Not Allowed | ||
| Assignments | 6 | 15 | Not Allowed | ||
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture hours | 3 | 14 | 42 | Assignments | 3 | 7 | 21 | |||
| Active labs | 2 | 14 | 28 | Home study | 1 | 14 | 14 | |||
| In-term exam study | 10 | 1 | 10 | Final exam study | 11 | 1 | 11 | |||
| Term project/presentation | 2 | 12 | 24 | |||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 27/03/2026 | |||||||||