CS525 Advanced Data Mining
CS525 Advanced Data Mining
Syllabus | International University of Sarajevo - Last Update on Feb 02, 2026
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Computer Sciences and Engineering
Emine Yaman
Course Lecturer
Course Objectives
This course aims to provide a research-oriented learning experience in advanced data mining by guiding students through the complete research cycle, from problem formulation and literature analysis to experimental design, result interpretation, and scientific writing. Students are expected to move beyond applying standard algorithms and instead develop the ability to design reproducible data mining experiments, critically evaluate model performance, identify research gaps, and produce a structured journal-style research paper. The course also introduces students to the academic peer-review process, fostering critical thinking, professional communication, and ethical research practices.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Research Methods: Information, Systems, and Contexts, Williamson, Kirsty; Johanson, Graeme (Eds.), 2nd Edition, 2018. Chandos Publishing(Elsevier).
Additional Literature
Data Mining, Charu C. Aggarwal, Springer, ISBN-13: 978-3319141411, ISBN-10: 9783319141411Teaching Methods
The course follows a research-based, project-driven format combining supervised research, structured literature analysis, and iterative paper development
Instruction is centered on weekly research presentations, methodological discussions, and continuous formative feedback to support the production of a publishable-quality study
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Course Introduction & Research vs Project | |
| 2 | Topic Exploration & Problem Definition | |
| 3 | Dataset / Mathematical Framework / Data Source Selection | |
| 4 | Literature Review Planning | |
| 5 | Initial Literature Review Presentation | |
| 6 | Contribution & Research Design | |
| 7 | Paper Writing Starts: Introduction Draft | |
| 8 | Methodology / Mathematical Model Writing | |
| 9 | Experimental Results / Theoretical Results | |
| 10 | Results Discussion & Paper Structuring | |
| 11 | Conclusion & Future Work Draft | |
| 12 | Full Draft Submission & Peer Review | |
| 13 | Reviewer Comments & Revision Planning | |
| 14 | Final Paper Revision & Response Letter | |
| 15 | Final Presentation & Research Reflection |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| CS525.1 | Course | Tuesday 17:00 - 19:50 | B F1.22 | - | - |
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Wednesday | 10:00 - 12:00 | A F1.34 | |
| Thursday | 10:00 - 12:00 | A F1.34 | |
| Friday | 10:00 - 12:00 | A F1.34 |
Assessment Methods and Criteria
Assessment Components
Final Paper
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Weekly Presentations
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Paper Review & Feedback Quality
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Participation
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
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)
Presentations
20 hours ⏳ (5 week × 4 h)
Preparing Proposal
25 hours ⏳ (1 week × 25 h)
Preparing project paper/presentation
45 hours ⏳ (1 week × 45 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 [CS525] 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 | |||||||||
| CS525 | Advanced Data Mining | 3 | 0 | 6 | Tue: 17:00-20:00 | |||||
| Prerequisite | None | It is a prerequisite to | - | |||||||
| Lecturer | Emine Yaman | Office Hours / Room / Phone | Wednesday: 10:00-12:00 Thursday: 10:00-12:00 Friday: 10:00-12:00 |
|||||||
| eyaman@ius.edu.ba | ||||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | This course aims to provide a research-oriented learning experience in advanced data mining by guiding students through the complete research cycle, from problem formulation and literature analysis to experimental design, result interpretation, and scientific writing. Students are expected to move beyond applying standard algorithms and instead develop the ability to design reproducible data mining experiments, critically evaluate model performance, identify research gaps, and produce a structured journal-style research paper. The course also introduces students to the academic peer-review process, fostering critical thinking, professional communication, and ethical research practices. |
|||||||||
| Textbook | Research Methods: Information, Systems, and Contexts, Williamson, Kirsty; Johanson, Graeme (Eds.), 2nd Edition, 2018. Chandos Publishing(Elsevier). | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | The course follows a research-based, project-driven format combining supervised research, structured literature analysis, and iterative paper development. Instruction is centered on weekly research presentations, methodological discussions, and continuous formative feedback to support the production of a publishable-quality study. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Course Introduction & Research vs Project | |||||||||
| Week 2 | Topic Exploration & Problem Definition | |||||||||
| Week 3 | Dataset / Mathematical Framework / Data Source Selection | |||||||||
| Week 4 | Literature Review Planning | |||||||||
| Week 5 | Initial Literature Review Presentation | |||||||||
| Week 6 | Contribution & Research Design | |||||||||
| Week 7 | Paper Writing Starts: Introduction Draft | |||||||||
| Week 8 | Methodology / Mathematical Model Writing | |||||||||
| Week 9 | Experimental Results / Theoretical Results | |||||||||
| Week 10 | Results Discussion & Paper Structuring | |||||||||
| Week 11 | Conclusion & Future Work Draft | |||||||||
| Week 12 | Full Draft Submission & Peer Review | |||||||||
| Week 13 | Reviewer Comments & Revision Planning | |||||||||
| Week 14 | Final Paper Revision & Response Letter | |||||||||
| Week 15 | Final Presentation & Research Reflection | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Paper | 1 | 45 | 1,2,3,4,5 | Not Allowed | |
| Semester Evaluation Components | |||||
| Weekly Presentations | 13 | 30 | 1,2,3,4,5 | Not Allowed | |
| Paper Review & Feedback Quality | 1 | 15 | 1,2,3,4,5 | Not Allowed | |
| Participation | 15 | 10 | 1,2,3,4,5 | 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 | |||
| Presentations | 4 | 5 | 20 | Preparing Proposal | 25 | 1 | 25 | |||
| Preparing project paper/presentation | 45 | 1 | 45 | |||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 24/02/2026 | |||||||||