SE504 Fuzzy Logic and Neural Networks
Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026
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Software Engineering
Ali Almisreb
Course Lecturer
Course Objectives
1. To learn what are Neural Networks. 2. To know how to apply Convolutional Neural Networks (CNN) in the computer vision area. 3. How to prepare data for CNN in Computer vision applications.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
[1] Trask, Andrew. Grokking deep learning. Manning Publications Co., 2019. [2] Morales, M. "Grokking Deep Reinforcement Learning." (2019).
Additional Literature
[1] Andrew Glassner, DEEP LEARNING: From Basics to Practice, The Imaginary Institute.2018. [2] Atienza, Rowel. Advanced Deep Learning with TensorFlow 2 and Keras, Packt Publishing Ltd, 2020. [3] Goodfellow, Ian, et al. Deep learning. Vol. 1. No. 2. Cambridge: MIT Press, 2016.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 Convolutional Neural Networks | Hands-on |
| 2 | Shallow Neural Network | Hands-on |
| 3 | Convolutional Neural Networks From Scratch-Mini Project 1 | Hands-on |
| 4 | Transfer Learning-Mini Project 2 | Hands-on |
| 5 | Object Detection-Mini Project 3 | Hands-on |
| 6 | Semantic Segmentation -Mini Project 4 | Hands-on |
| 7 | Recurrent Neural Networks | Hands-on |
| 8 | Modern Recurrent Neural Networks | Hands-on |
| 9 | Optimization Algorithms | Hands-on |
| 10 | Natural Language Processing: Pretraining-Mini Project 5 | Hands-on |
| 11 | Natural Language Processing: Applications | Hands-on |
| 12 | Generative Adversarial Networks | Hands-on |
| 13 | Autoencoder-Mini Project 6 | Hands-on |
| 14 | Quantum Convolutional Neural Networks | Hands-on |
| 15 | Quantum Convolutional Neural Networks: Application | Hands-on |
Course Schedule (All Sections)
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Thursday | 09:00 - 11:55 | A F2.6 | |
| Friday | 09:00 - 11:55 | A F2.6 |
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes :
Mini Project 1
AI: Not AllowedAlignment with Learning Outcomes :
Mini Project 2
AI: Not AllowedAlignment with Learning Outcomes :
Mini Project 3
AI: Not AllowedAlignment with Learning Outcomes :
Mini Project 4
AI: Not AllowedAlignment with Learning Outcomes :
Mini Project 5
AI: Not AllowedAlignment with Learning Outcomes :
Mini Project 6
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:
Final Exam
30 hours ⏳ (3 week × 10 h)
Mini Project 1
20 hours ⏳ (2 week × 10 h)
Mini Project 2
20 hours ⏳ (2 week × 10 h)
Mini Project 3
20 hours ⏳ (2 week × 10 h)
Mini Project 4
20 hours ⏳ (2 week × 10 h)
Mini Project 5
20 hours ⏳ (2 week × 10 h)
Mini Project 6
20 hours ⏳ (2 week × 10 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 [SE504] 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
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| SE504 | Fuzzy Logic and Neural Networks | 3 | 0 | 6 | Fri 17:00‐20:00 | |||||
| Prerequisite | None | It is a prerequisite to | - | |||||||
| Lecturer | Ali Almisreb | Office Hours / Room / Phone | Thursday: 9:00-11:55 Friday: 9:00-11:55 |
|||||||
| aalmisreb@ius.edu.ba | ||||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | 1. To learn what are Neural Networks. 2. To know how to apply Convolutional Neural Networks (CNN) in the computer vision area. 3. How to prepare data for CNN in Computer vision applications. |
|||||||||
| Textbook | [1] Trask, Andrew. Grokking deep learning. Manning Publications Co., 2019. [2] Morales, M. "Grokking Deep Reinforcement Learning." (2019). | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| 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 | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction to Convolutional Neural Networks | Hands-on | ||||||||
| Week 2 | Shallow Neural Network | Hands-on | ||||||||
| Week 3 | Convolutional Neural Networks From Scratch-Mini Project 1 | Hands-on | ||||||||
| Week 4 | Transfer Learning-Mini Project 2 | Hands-on | ||||||||
| Week 5 | Object Detection-Mini Project 3 | Hands-on | ||||||||
| Week 6 | Semantic Segmentation -Mini Project 4 | Hands-on | ||||||||
| Week 7 | Recurrent Neural Networks | Hands-on | ||||||||
| Week 8 | Modern Recurrent Neural Networks | Hands-on | ||||||||
| Week 9 | Optimization Algorithms | Hands-on | ||||||||
| Week 10 | Natural Language Processing: Pretraining-Mini Project 5 | Hands-on | ||||||||
| Week 11 | Natural Language Processing: Applications | Hands-on | ||||||||
| Week 12 | Generative Adversarial Networks | Hands-on | ||||||||
| Week 13 | Autoencoder-Mini Project 6 | Hands-on | ||||||||
| Week 14 | Quantum Convolutional Neural Networks | Hands-on | ||||||||
| Week 15 | Quantum Convolutional Neural Networks: Application | Hands-on | ||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 40 | Not Allowed | ||
| Semester Evaluation Components | |||||
| Mini Project 1 | 1 | 10 | Not Allowed | ||
| Mini Project 2 | 1 | 10 | Not Allowed | ||
| Mini Project 3 | 1 | 10 | Not Allowed | ||
| Mini Project 4 | 1 | 10 | Not Allowed | ||
| Mini Project 5 | 1 | 10 | Not Allowed | ||
| Mini Project 6 | 1 | 10 | Not Allowed | ||
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Final Exam | 10 | 3 | 30 | Mini Project 1 | 10 | 2 | 20 | |||
| Mini Project 2 | 10 | 2 | 20 | Mini Project 3 | 10 | 2 | 20 | |||
| Mini Project 4 | 10 | 2 | 20 | Mini Project 5 | 10 | 2 | 20 | |||
| Mini Project 6 | 10 | 2 | 20 | |||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 27/03/2026 | |||||||||