AID401 Deep Learning
AID401 Deep Learning
Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026
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Artificial Intelligence and Data Engineering
Course Objectives
1. To learn the basics of deep learning, including the key concepts, terminology, and tools. 2. To gain hands-on experience in building and training deep neural networks using Keras and TensorFlow, two popular deep learning frameworks. 3. To explore various types of deep neural networks, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and transformers. 4. To apply deep learning techniques to various domains such as computer vision, natural language processing, speech recognition, and reinforcement learning. 5. To understand the current challenges and limitations of deep learning and how to overcome them.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Required textbooks for this course are: [1] Chollet, F. (2017). Deep learning with Python. Manning Publications Co. [2] Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O’Reilly Media, Inc.
Additional Literature
Recommended textbooks for this course are: [1] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. [2] Raschka, S., & Mirjalili, V. (2019). Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow 2. Packt Publishing.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 Deep Learning and Keras | DLP: Ch. 1; HML: Ch. 10 & 11 |
| 2 | Neural Networks Basics and TensorFlow Assignment 1: Neural Networks with Keras and TensorFlow | DLP: Ch. 2; HML: Ch. 12 & 13 |
| 3 | Convolutional Neural Networks for Computer Vision | DLP: Ch. 5; HML: Ch. 14 & 15 |
| 4 | Recurrent Neural Networks for Natural Language Processing Assignment 2: Image Classification with CNNs | DLP: Ch. 6; HML: Ch. 16 & 17 |
| 5 | Generative Adversarial Networks for Image Generation | DLP: Ch. 8; HML: Ch. 18 & 19 |
| 6 | Transformers for Sequence Modeling and Attention Mechanisms Assignment 3: Text Generation with RNNs | DLP: Ch. 11; HML: Ch. 20 & 21 & Appendix A & B & C |
| 7 | Midterm exam | |
| 8 | Machine Learning for Cybersecurity | Hands-on |
| 9 | Reinforcement Learning and Deep Q-Networks for Game Playing Assignment 4: Image Generation with GANs | DLP: Ch. 18; HML: Ch. 22; Appendix A |
| 10 | Advanced Topics in Deep Learning (I) - Self-Supervised Learning and Contrastive Learning | DLP: Ch. 12; HML: Ch. 23; Appendix B |
| 11 | Advanced Topics in Deep Learning (II) - Graph Neural Networks and Geometric Deep Learning Assignment 5: Transformers for Sequence Modeling and Attention Mechanisms | DLP: Ch. 14; HML: Ch. 24; Appendix C |
| 12 | Advanced Topics in Deep Learning (III) - Meta-Learning and Few-Shot Learning | DLP: Ch. 15; HML: Ch. 25; Appendix D |
| 13 | Deep Reinforcement Learning | Hands-on |
| 14 | Deep Learning for Computer Vision | Hands-on |
| 15 | Final Project Presentations | Final Project Report and Presentation |
Course Schedule (All Sections)
Office Hours & Room
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes :
Final Project
AI: Not AllowedAlignment with Learning Outcomes :
Assignments
AI: Not AllowedAlignment with Learning Outcomes :
Quizzes
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)
Assignment 1, and Quiz 1
20 hours ⏳ (2 week × 10 h)
Assignment 2
20 hours ⏳ (2 week × 10 h)
Assignment 3, and Quiz 2
20 hours ⏳ (2 week × 10 h)
Assignment 4
20 hours ⏳ (2 week × 10 h)
Assignment 5, and Quiz 3
20 hours ⏳ (2 week × 10 h)
Final Project
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 [AID401] 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 | |||||||||
| AID401 | Deep Learning | 3 | 2 | 6 | ||||||
| Prerequisite | CS404 | It is a prerequisite to | - | |||||||
| Lecturer | Office Hours / Room / Phone | Currently not available |
||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | 1. To learn the basics of deep learning, including the key concepts, terminology, and tools. 2. To gain hands-on experience in building and training deep neural networks using Keras and TensorFlow, two popular deep learning frameworks. 3. To explore various types of deep neural networks, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and transformers. 4. To apply deep learning techniques to various domains such as computer vision, natural language processing, speech recognition, and reinforcement learning. 5. To understand the current challenges and limitations of deep learning and how to overcome them. |
|||||||||
| Textbook | Required textbooks for this course are: [1] Chollet, F. (2017). Deep learning with Python. Manning Publications Co. [2] Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O’Reilly Media, Inc. | |||||||||
| Additional Literature |
|
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| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
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| 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 Deep Learning and Keras | DLP: Ch. 1; HML: Ch. 10 & 11 | ||||||||
| Week 2 | Neural Networks Basics and TensorFlow Assignment 1: Neural Networks with Keras and TensorFlow | DLP: Ch. 2; HML: Ch. 12 & 13 | ||||||||
| Week 3 | Convolutional Neural Networks for Computer Vision | DLP: Ch. 5; HML: Ch. 14 & 15 | ||||||||
| Week 4 | Recurrent Neural Networks for Natural Language Processing Assignment 2: Image Classification with CNNs | DLP: Ch. 6; HML: Ch. 16 & 17 | ||||||||
| Week 5 | Generative Adversarial Networks for Image Generation | DLP: Ch. 8; HML: Ch. 18 & 19 | ||||||||
| Week 6 | Transformers for Sequence Modeling and Attention Mechanisms Assignment 3: Text Generation with RNNs | DLP: Ch. 11; HML: Ch. 20 & 21 & Appendix A & B & C | ||||||||
| Week 7 | Midterm exam | |||||||||
| Week 8 | Machine Learning for Cybersecurity | Hands-on | ||||||||
| Week 9 | Reinforcement Learning and Deep Q-Networks for Game Playing Assignment 4: Image Generation with GANs | DLP: Ch. 18; HML: Ch. 22; Appendix A | ||||||||
| Week 10 | Advanced Topics in Deep Learning (I) - Self-Supervised Learning and Contrastive Learning | DLP: Ch. 12; HML: Ch. 23; Appendix B | ||||||||
| Week 11 | Advanced Topics in Deep Learning (II) - Graph Neural Networks and Geometric Deep Learning Assignment 5: Transformers for Sequence Modeling and Attention Mechanisms | DLP: Ch. 14; HML: Ch. 24; Appendix C | ||||||||
| Week 12 | Advanced Topics in Deep Learning (III) - Meta-Learning and Few-Shot Learning | DLP: Ch. 15; HML: Ch. 25; Appendix D | ||||||||
| Week 13 | Deep Reinforcement Learning | Hands-on | ||||||||
| Week 14 | Deep Learning for Computer Vision | Hands-on | ||||||||
| Week 15 | Final Project Presentations | Final Project Report and Presentation | ||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 40 | Not Allowed | ||
| Semester Evaluation Components | |||||
| Final Project | 1 | 20 | Not Allowed | ||
| Assignments | 5 | 25 | Not Allowed | ||
| Quizzes | 3 | 15 | Not Allowed | ||
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Final Exam | 10 | 3 | 30 | Assignment 1, and Quiz 1 | 10 | 2 | 20 | |||
| Assignment 2 | 10 | 2 | 20 | Assignment 3, and Quiz 2 | 10 | 2 | 20 | |||
| Assignment 4 | 10 | 2 | 20 | Assignment 5, and Quiz 3 | 10 | 2 | 20 | |||
| Final Project | 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 | |||||||||