BUS671 Advanced Topics in Business
BUS671 Advanced Topics in Business
Syllabus | International University of Sarajevo - Last Update on Oct 10, 2025
Syllabus Quick Jump
Search and navigate to any syllabus instantly
Department of Economics and Management
Admir Mešković
Course Lecturer
Course Objectives
The main objective is to face students with current and challenging issues in business and to make them reflect on the research opportunities that those challenges provide.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Special Readings.
Additional Literature
Supplemental materialsTeaching Methods
The methods include lectures (which may involve PowerPoint presentations
Videos
And audio aids)
Student presentations
Projects
And class discussions.
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction to AI applications in Business | |
| 2 | Big Data and Artificial Intelligence | |
| 3 | Training and Evaluating Machine Learning Algorithms | |
| 4 | ML Application and Emerging Methods | |
| 5 | Generative AI | |
| 6 | AI and the Customer Journey | |
| 7 | AI Applications in Finance | |
| 8 | AI Applications in Marketing | |
| 9 | AI Applications in Finance II | |
| 10 | The Promise and Potential of AI in HR | |
| 11 | Economics of AI and AI Innovation | |
| 12 | Algorithmic Bias and Fairness | |
| 13 | AI Governance and Explainable AI | |
| 14 | AI Strategy | |
| 15 | Presentations |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| BUS671 .1 | Course | Thursday 17:00 - 19:50 | B F1.2 - Class/ECON Lab | - | - |
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Monday | 15:00 - 17:00 | B F1.12 | |
| Tuesday | 12:00 - 15:00 | B F1.12 |
Assessment Methods and Criteria
Assessment Components
Final project
AI: Not AllowedAlignment with Learning Outcomes : 4 5
Research paper presentation and discussion
AI: Not AllowedAlignment with Learning Outcomes : 1 4
Case study analysis
AI: Not AllowedAlignment with Learning Outcomes : 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:
Lecture Hours
45 hours ⏳ (15 week × 3 h)
Home study
45 hours ⏳ (15 week × 3 h)
Case study
20 hours ⏳ (1 week × 20 h)
Final paper
40 hours ⏳ (10 week × 4 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 [BUS671 ] 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 Oct 10, 2025 | International University of Sarajevo
Print Syllabus
Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| BUS671 | Advanced Topics in Business | 3 | 0 | 6 | ||||||
| Prerequisite | None | It is a prerequisite to | - | |||||||
| Lecturer | Admir Mešković | Office Hours / Room / Phone | Monday: 15:00-17:00 Tuesday: 12:00-15:00 |
|||||||
| ameskovic@ius.edu.ba | ||||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | The main objective is to face students with current and challenging issues in business and to make them reflect on the research opportunities that those challenges provide. |
|||||||||
| Textbook | Special Readings. | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | The methods include lectures (which may involve PowerPoint presentations, videos, and audio aids), student presentations, projects, and class discussions. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction to AI applications in Business | |||||||||
| Week 2 | Big Data and Artificial Intelligence | |||||||||
| Week 3 | Training and Evaluating Machine Learning Algorithms | |||||||||
| Week 4 | ML Application and Emerging Methods | |||||||||
| Week 5 | Generative AI | |||||||||
| Week 6 | AI and the Customer Journey | |||||||||
| Week 7 | AI Applications in Finance | |||||||||
| Week 8 | AI Applications in Marketing | |||||||||
| Week 9 | AI Applications in Finance II | |||||||||
| Week 10 | The Promise and Potential of AI in HR | |||||||||
| Week 11 | Economics of AI and AI Innovation | |||||||||
| Week 12 | Algorithmic Bias and Fairness | |||||||||
| Week 13 | AI Governance and Explainable AI | |||||||||
| Week 14 | AI Strategy | |||||||||
| Week 15 | Presentations | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final project | 1 | 35 | 4, 5 | Not Allowed | |
| Semester Evaluation Components | |||||
| Research paper presentation and discussion | 1 | 25 | 1,4 | Not Allowed | |
| Case study analysis | 1 | 40 | 2,3 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 3 | 15 | 45 | Home study | 3 | 15 | 45 | |||
| Case study | 20 | 1 | 20 | Final paper | 4 | 10 | 40 | |||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 09/10/2025 | |||||||||