CS511 Advanced Artificial Intelligence
CS511 Advanced Artificial Intelligence
Syllabus | International University of Sarajevo - Last Update on Feb 02, 2026
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Computer Sciences and Engineering
Ali Almisreb
Course Lecturer
Course Objectives
The course will cover both basic and advanced artificial intelligence techniques in depth. The course will consist of a mixture of lectures by the instructor and a detailed analysis of selected papers by all participants. Each student is also expected to gain hands-on experience by carrying out a semester-long project on their topic of choice. The course aims to:
- Introduce the state-of-art methods in deep learning
- Fimilarize the students with the most recent programming platforms for deep learning
- Apply deep learning methods for real-world applications
- Explore the recent research articles in the deep learning research area
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Additional Literature
Artificial Intelligence: A Modern Approach (Third Edition), Stuart Russell and Peter Norvig, Prentice Hall, 1994. Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT Press, 2016.Teaching Methods
I will lecture one week and following week each student is expected to be prepared for discussion about related papers
Students are expected to read the required materials and participate in the discussions
Students are also expected to carry-out a semester long project on a problem of their choice
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction to Advanced AI | Hands-on |
| 2 | Deep Learning I: Foundations: Neural networks, architectures, optimization | Hands-on |
| 3 | Deep Learning II: Advanced Techniques:Convolutional networks | Hands-on |
| 4 | Deep Learning III: Advanced Techniques:recurrent networks | Hands-on |
| 5 | Deep Learning IV: Advanced Techniques: deep reinforcement learning | Hands-on |
| 6 | Natural Language Processing I: Fundamentals: Word embeddings, sequence models, language modeling | Hands-on |
| 7 | Natural Language Processing II: Advanced Applications: Machine translation | Hands-on |
| 8 | Natural Language Processing III: Advanced Applications: question answering, dialogue systems-Project 1 | Hands-on |
| 9 | Computer Vision I: Image understanding: Object detection, image segmentation | Hands-on |
| 10 | Computer Vision II: Video and advanced applications: Action recognition, image generation | Hands-on |
| 11 | Computer Vision III: Video and advanced applications: medical imaging | Hands-on |
| 12 | Generative AI I: Fundamentals: Generative adversarial networks, variational autoencoders | Hands-on |
| 13 | Generative AI II: Applications and challenges: Text generation, music composition | Hands-on |
| 14 | Generative AI II: Applications and challenges: creative content generation | Hands-on |
| 15 | Explainable AI (XAI): Interpretability methods, fairness and bias mitigation- Project 2 | Hands-on |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| CS511.1 | Course | Wednesday 17:00 - 19:50 | A F1.23 | - | - |
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 : 1 2 3 4
Research Paper 1
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4
Research Paper 2
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4
Assignments
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4
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
45 hours ⏳ (15 week × 3 h)
Home study
15 hours ⏳ (15 week × 1 h)
Preparation for Paperiscussion
35 hours ⏳ (5 week × 7 h)
Project
45 hours ⏳ (3 week × 15 h)
Assignments
10 hours ⏳ (10 week × 1 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 [CS511] 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 Feb 02, 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 | |||||||||
| CS511 | Advanced Artificial Intelligence | 3 | 0 | 6 | ||||||
| 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 | The course will cover both basic and advanced artificial intelligence techniques in depth. The course will consist of a mixture of lectures by the instructor and a detailed analysis of selected papers by all participants. Each student is also expected to gain hands-on experience by carrying out a semester-long project on their topic of choice. The course aims to: Introduce the state-of-art methods in deep learning Fimilarize the students with the most recent programming platforms for deep learning Apply deep learning methods for real-world applications Explore the recent research articles in the deep learning research area |
|||||||||
| Textbook | Raschka, Sebastian, et al. Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python. Packt Publishing Ltd, 2022. Edward Raff, Inside Deep Learning, Math, Algorithms, Models, MANNING, 2022. Buduma, Nithin, Nikhil Buduma, and Joe Papa. Fundamentals of deep learning. " O'Reilly Media, Inc.", 2022. Liquet, B., Moka, S., & Nazarathy, Y. (2024). The Mathematical Engineering of Deep Learning. CRC Press. Online: https://deeplearningmath.org/ | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
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| Teaching Methods | I will lecture one week and following week each student is expected to be prepared for discussion about related papers. Students are expected to read the required materials and participate in the discussions. Students are also expected to carry-out a semester long project on a problem of their choice. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction to Advanced AI | Hands-on | ||||||||
| Week 2 | Deep Learning I: Foundations: Neural networks, architectures, optimization | Hands-on | ||||||||
| Week 3 | Deep Learning II: Advanced Techniques:Convolutional networks | Hands-on | ||||||||
| Week 4 | Deep Learning III: Advanced Techniques:recurrent networks | Hands-on | ||||||||
| Week 5 | Deep Learning IV: Advanced Techniques: deep reinforcement learning | Hands-on | ||||||||
| Week 6 | Natural Language Processing I: Fundamentals: Word embeddings, sequence models, language modeling | Hands-on | ||||||||
| Week 7 | Natural Language Processing II: Advanced Applications: Machine translation | Hands-on | ||||||||
| Week 8 | Natural Language Processing III: Advanced Applications: question answering, dialogue systems-Project 1 | Hands-on | ||||||||
| Week 9 | Computer Vision I: Image understanding: Object detection, image segmentation | Hands-on | ||||||||
| Week 10 | Computer Vision II: Video and advanced applications: Action recognition, image generation | Hands-on | ||||||||
| Week 11 | Computer Vision III: Video and advanced applications: medical imaging | Hands-on | ||||||||
| Week 12 | Generative AI I: Fundamentals: Generative adversarial networks, variational autoencoders | Hands-on | ||||||||
| Week 13 | Generative AI II: Applications and challenges: Text generation, music composition | Hands-on | ||||||||
| Week 14 | Generative AI II: Applications and challenges: creative content generation | Hands-on | ||||||||
| Week 15 | Explainable AI (XAI): Interpretability methods, fairness and bias mitigation- Project 2 | Hands-on | ||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 40 | 1,2,3,4 | Not Allowed | |
| Semester Evaluation Components | |||||
| Research Paper 1 | 1 | 25 | 1,2,3,4 | Not Allowed | |
| Research Paper 2 | 1 | 25 | 1,2,3,4 | Not Allowed | |
| Assignments | 10 | 10 | 1,2,3,4 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 3 | 15 | 45 | Home study | 1 | 15 | 15 | |||
| Preparation for Paperiscussion | 7 | 5 | 35 | Project | 15 | 3 | 45 | |||
| Assignments | 1 | 10 | 10 | |||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 25/02/2026 | |||||||||