CS404 Artificial Intelligence
CS404 Artificial Intelligence
Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026
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Computer Sciences and Engineering
Ali Almisreb
Course Lecturer
Course Objectives
1. Introduce students to the foundational principles of Artificial Intelligence, including intelligent agents, problem-solving strategies, and search algorithms. 2. Develop a strong understanding of knowledge representation and reasoning, enabling students to model and infer information in uncertain environments. 3. Equip students with practical skills in machine learning, covering genetic algorithms, supervised learning, and neural network training using modern frameworks. 4. Explore advanced deep learning architectures and applications, including convolutional, recurrent, and graph neural networks, with hands-on experience in NLP and computer vision. 5. Foster critical thinking about the ethical and societal implications of AI, encouraging responsible design and deployment of intelligent systems.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
1. Russell, S., & Norvig, P. (2020), Artificial Intelligence: A Modern Approach (4th Edition), Pearson Education. 2. Szeliski, R. (2022), Computer Vision: Algorithms and Applications (2nd Edition), Springer. 3. Bernard, H. & Guss, D. (2020), Neural Networks from Scratch in Python: Understanding Deep Learning through Theory and Practice, Harrison Kinsley.
Additional Literature
Zhang, Aston, et al. "Dive into deep learning." arXiv preprint arXiv:2106.11342 (2021).Teaching Methods
Theoretical and practical lessons will take place in the classrooms
Additionally, lab sessions will be conducted in one of the computer labs at IUS
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction to Artificial Intelligence | AIMA Ch. 1 |
| 2 | Intelligent Agents, Problem Solving in Artificial Intelligence | AIMA Ch. 2–3 |
| 3 | Search in Complex Environments | AIMA Ch. 4 |
| 4 | Knowledge Representation | AIMA Ch. 10–12 |
| 5 | Uncertain Knowledge and Reasoning | AIMA Ch. 13–14 |
| 6 | Learning: Artificial Neural Networks I | Hands-on |
| 7 | Learning: Artificial Neural Networks II | NNFS Ch. 1–6 |
| 8 | Project 1 Presentation and Midterm | |
| 9 | Backpropagation and Optimization | NNFS Ch. 7–10 |
| 10 | Graph Neural Networks | AIMA Ch. 20 |
| 11 | Deep Learning Architectures (CNNs, RNNs, Transformers) | NNFS Ch. 11–14, AIMA Ch. 22 |
| 12 | Natural Language Processing and Transformers | AIMA Ch. 24–25 |
| 13 | Reinforcement Learning and Applications | AIMA Ch. 23 |
| 14 | AI in Robotics and Computer Vision | AIMA Ch. 26–27, Szeliski Ch. 3–5 |
| 15 | Project 2 Presentation & Ethics, Safety, and the Future of AI | AIMA Ch. 28–29 |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| CS404.1 | Course | Tuesday 09:00 - 11:50 | A F1.10 | - | - |
| CS404.1 | Tutorial | Wednesday 12:00 - 13:50 | A F1.4 - Class/Laboratory | - | - |
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
Project 1
AI: Consult InstructorAlignment with Learning Outcomes : 1 2
Project 2
AI: Consult InstructorAlignment with Learning Outcomes : 3 4
Labs
AI: Consult InstructorAlignment with Learning Outcomes : 1 2 3 4
Midterm
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3
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 studies
22 hours ⏳ (2 week × 11 h)
Project1-ANN
12 hours ⏳ (6 week × 2 h)
project2-CNN
32 hours ⏳ (8 week × 4 h)
labs
14 hours ⏳ (7 week × 2 h)
Midterm
70 hours ⏳ (7 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 [CS404] 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 | |||||||||
| CS404 | Artificial Intelligence | 3 | 2 | 6 | Tuesday: 9:00-11:50 | |||||
| Prerequisite | MATH201, CS103 | It is a prerequisite to | AID401 | |||||||
| 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. Introduce students to the foundational principles of Artificial Intelligence, including intelligent agents, problem-solving strategies, and search algorithms. 2. Develop a strong understanding of knowledge representation and reasoning, enabling students to model and infer information in uncertain environments. 3. Equip students with practical skills in machine learning, covering genetic algorithms, supervised learning, and neural network training using modern frameworks. 4. Explore advanced deep learning architectures and applications, including convolutional, recurrent, and graph neural networks, with hands-on experience in NLP and computer vision. 5. Foster critical thinking about the ethical and societal implications of AI, encouraging responsible design and deployment of intelligent systems. |
|||||||||
| Textbook | 1. Russell, S., & Norvig, P. (2020), Artificial Intelligence: A Modern Approach (4th Edition), Pearson Education. 2. Szeliski, R. (2022), Computer Vision: Algorithms and Applications (2nd Edition), Springer. 3. Bernard, H. & Guss, D. (2020), Neural Networks from Scratch in Python: Understanding Deep Learning through Theory and Practice, Harrison Kinsley. | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
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| Teaching Methods | Theoretical and practical lessons will take place in the classrooms. Additionally, lab sessions will be conducted in one of the computer labs at IUS. . | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction to Artificial Intelligence | AIMA Ch. 1 | ||||||||
| Week 2 | Intelligent Agents, Problem Solving in Artificial Intelligence | AIMA Ch. 2–3 | ||||||||
| Week 3 | Search in Complex Environments | AIMA Ch. 4 | ||||||||
| Week 4 | Knowledge Representation | AIMA Ch. 10–12 | ||||||||
| Week 5 | Uncertain Knowledge and Reasoning | AIMA Ch. 13–14 | ||||||||
| Week 6 | Learning: Artificial Neural Networks I | Hands-on | ||||||||
| Week 7 | Learning: Artificial Neural Networks II | NNFS Ch. 1–6 | ||||||||
| Week 8 | Project 1 Presentation and Midterm | |||||||||
| Week 9 | Backpropagation and Optimization | NNFS Ch. 7–10 | ||||||||
| Week 10 | Graph Neural Networks | AIMA Ch. 20 | ||||||||
| Week 11 | Deep Learning Architectures (CNNs, RNNs, Transformers) | NNFS Ch. 11–14, AIMA Ch. 22 | ||||||||
| Week 12 | Natural Language Processing and Transformers | AIMA Ch. 24–25 | ||||||||
| Week 13 | Reinforcement Learning and Applications | AIMA Ch. 23 | ||||||||
| Week 14 | AI in Robotics and Computer Vision | AIMA Ch. 26–27, Szeliski Ch. 3–5 | ||||||||
| Week 15 | Project 2 Presentation & Ethics, Safety, and the Future of AI | AIMA Ch. 28–29 | ||||||||
| 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 | |||||
| Project 1 | 1 | 10 | 1,2 | Consult Instructor | |
| Project 2 | 1 | 10 | 3,4 | Consult Instructor | |
| Labs | 5 | 10 | 1,2,3,4 | Consult Instructor | |
| Midterm | 1 | 30 | 1, 2, 3 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Final exam studies | 11 | 2 | 22 | Project1-ANN | 2 | 6 | 12 | |||
| project2-CNN | 4 | 8 | 32 | labs | 2 | 7 | 14 | |||
| Midterm | 10 | 7 | 70 | 0 | ||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 27/03/2026 | |||||||||