CS207 Analysis of Algorithms
Syllabus | International University of Sarajevo - Last Update on Apr 04, 2026
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Computer Sciences and Engineering
Babatunde Kazeem Oladejo
Course Lecturer
Course Objectives
The objective of the course is to introduce and train students in the design and analysis of algorithms in the program implementation. It demonstrates the analysis of the computational complexity of programs along with their comparative analysis. In addition to the design of numerous algorithms, the course incorporates a significant emphasis on mathematical principles.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Data Structures and Abstractions with Java, 3rd Edition, Frank Carrano, ISBN: 9780136100911 AND -- Algorithm Design, 1st edition, Jon Kleinberg and Eva Tardos, ISBN: 9780137546350
Additional Literature
Absolute Java, 6th Edition, Walter Savitch, ISBN: 9780133947793Teaching Methods
Lectures
Tutorials
Class discussions with examples
Project.
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Course Introduction | Kleinberg_Tardos chp 2, Carrano chp 4 |
| 2 | Recursion Algorithms | Savitch chp 11 |
| 3 | Divide and Conquer / Sorting Algorithms | Kleinberg_Tardos chp 5; Carrano chps 8 and 9 |
| 4 | No Lecture (Holiday), Lab Active | N/A |
| 5 | Big O Analysis | Carrano chps 4 and 9 |
| 6 | Searching Algorithms | Carrano chps 2, 3 and 18 |
| 7 | Trees (Binary, AVL) | Carrano chps 23 and 27 |
| 8 | Midterm Exam | N/A |
| 9 | Graph Theory and Spanning Trees (DFS and BFS) | Carrano chp 28, Kleinberg_Tardos chp 3; |
| 10 | No Lecture (BiH holiday), Lab Active | N/A |
| 11 | Shortest Path Algorithms | Carrano chp 28, Kleinberg_Tardos chp 4; |
| 12 | Greedy Algorithms | Kleinberg_Tardos chp 4 |
| 13 | Dynamic Programming | Kleinberg_Tardos chp 6 |
| 14 | No Lecture, No Lab (BiH holiday) | N/A |
| 15 | Final Review | N/A |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| CS207.1 | Tutorial | Wednesday 09:00 - 10:50 | A F1.18 - Computer Lab | - | - |
| CS207.1 | Course | Friday 09:00 - 11:50 | A F2.8 - Classroom | - | - |
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Wednesday | 14:00 - 17:00 | A F1.16 | |
| Thursday | 11:00 - 13:00 | A F1.16 |
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5 6
Midterm
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3
Quizzes
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5 6
Project
AI: Consult InstructorAlignment with Learning Outcomes : 1 2 3 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)
Tutorials
30 hours ⏳ (15 week × 2 h)
Midterm Study
18 hours ⏳ (1 week × 18 h)
Final Study
30 hours ⏳ (2 week × 15 h)
Project
15 hours ⏳ (15 week × 1 h)
Quizzes
12 hours ⏳ (2 week × 6 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 [CS207] 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 Apr 04, 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 | |||||||||
| CS207 | Analysis of Algorithms | 3 | 2 | 6 | ||||||
| Prerequisite | CS206, MATH204 | It is a prerequisite to | - | |||||||
| Lecturer | Babatunde Kazeem Oladejo | Office Hours / Room / Phone | Wednesday: 14:00-17:00 Thursday: 11:00-13:00 |
|||||||
| koladejo@ius.edu.ba | ||||||||||
| Assistant | Nedzla Sehovic | Assistant E-mail | 250302374@student.ius.edu.ba | |||||||
| Course Objectives | The objective of the course is to introduce and train students in the design and analysis of algorithms in the program implementation. It demonstrates the analysis of the computational complexity of programs along with their comparative analysis. In addition to the design of numerous algorithms, the course incorporates a significant emphasis on mathematical principles. | |||||||||
| Textbook | Data Structures and Abstractions with Java, 3rd Edition, Frank Carrano, ISBN: 9780136100911 AND -- Algorithm Design, 1st edition, Jon Kleinberg and Eva Tardos, ISBN: 9780137546350 | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | Lectures, Tutorials, Class discussions with examples, Project. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Course Introduction | Kleinberg_Tardos chp 2, Carrano chp 4 | ||||||||
| Week 2 | Recursion Algorithms | Savitch chp 11 | ||||||||
| Week 3 | Divide and Conquer / Sorting Algorithms | Kleinberg_Tardos chp 5; Carrano chps 8 and 9 | ||||||||
| Week 4 | No Lecture (Holiday), Lab Active | N/A | ||||||||
| Week 5 | Big O Analysis | Carrano chps 4 and 9 | ||||||||
| Week 6 | Searching Algorithms | Carrano chps 2, 3 and 18 | ||||||||
| Week 7 | Trees (Binary, AVL) | Carrano chps 23 and 27 | ||||||||
| Week 8 | Midterm Exam | N/A | ||||||||
| Week 9 | Graph Theory and Spanning Trees (DFS and BFS) | Carrano chp 28, Kleinberg_Tardos chp 3; | ||||||||
| Week 10 | No Lecture (BiH holiday), Lab Active | N/A | ||||||||
| Week 11 | Shortest Path Algorithms | Carrano chp 28, Kleinberg_Tardos chp 4; | ||||||||
| Week 12 | Greedy Algorithms | Kleinberg_Tardos chp 4 | ||||||||
| Week 13 | Dynamic Programming | Kleinberg_Tardos chp 6 | ||||||||
| Week 14 | No Lecture, No Lab (BiH holiday) | N/A | ||||||||
| Week 15 | Final Review | N/A | ||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 40 | 1,2,3,4,5,6 | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm | 1 | 30 | 1,2,3 | Not Allowed | |
| Quizzes | 2 | 20 | 1,2,3,4,5,6 | Not Allowed | |
| Project | 1 | 10 | 1,2,3,5 | Consult Instructor | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 3 | 15 | 45 | Tutorials | 2 | 15 | 30 | |||
| Midterm Study | 18 | 1 | 18 | Final Study | 15 | 2 | 30 | |||
| Project | 1 | 15 | 15 | Quizzes | 6 | 2 | 12 | |||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 21/04/2026 | |||||||||