MATH201 Linear Algebra
MATH201 Linear Algebra
Syllabus | International University of Sarajevo - Last Update on Feb 02, 2026
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Faculty of Engineering and Natural Sciences
Leila Miller
Course Lecturer
Course Objectives
The goal of the course is to explain the main concepts and fundamental processes of linear systems of algebraic equations and applications of the matrix theory to everyday problems. The course will provide all necessary material related linear algebra and after taking this course, students will realize linear algebra and linear transformations have impacts on our lives in many ways and will almost certainly become more important in future.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Linear Algebra, L. Miller, M. Can
Additional Literature
Linear Algebra Problem Book L. MillerTeaching Methods
Class discussions with examples
Active tutorial sessions for engaged learning and continuous feedback on progress
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Linear Systems of Equations | 1.1, 1.2, 1.3 |
| 2 | Linear Systems of Equations | 1.4 |
| 3 | Vector Algebra | 2.1, 2.2, 2.3, 2.4, 2.5 |
| 4 | Real Vector Spaces. FIRST IN-TERM EXAM | 3.1, 3.2, 3.3 |
| 5 | Real Vector Spaces | 3.4, 3.5, 3.6 |
| 6 | Inner Product Spaces | 4.1, 4.2 |
| 7 | Inner Product Spaces MIDTERM EXAM | 4.3 |
| 8 | Linear Transformations and Matrices | 5.1, 5.2 |
| 9 | Linear Transformations and Matrices | 5.3 |
| 10 | Eigenvalues and Eigenvectors SECOND IN-TERM EXAM | 6.1 |
| 11 | Eigenvalues and Eigenvectors | 6.2 |
| 12 | Eigenvalues and Eigenvectors | 6.3 |
| 13 | Eigenvalues and Eigenvectors | 6.3 |
| 14 | Applications | 7.1, 7.2, 7.3 |
| 15 | Applications | 7.4, 7.5 |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| MATH201.1 | Course | Tuesday 09:00 - 11:50 | A F2.14 - Amphitheater II | - | - |
| MATH201.2 | Course | Wednesday 12:00 - 14:50 | A F2.14 - Amphitheater II | - | - |
| MATH201.3 | Course | Monday 12:00 - 14:50 | A F2.8 - Classroom | - | - |
| MATH201.1 | Tutorial | Thursday 15:00 - 16:50 | B F2.5 | - | - |
| MATH201.2 | Tutorial | Friday 15:00 - 16:50 | B F2.5 | - | - |
| MATH201.3 | Tutorial | Thursday 12:00 - 13:50 | A F1.23 | - | - |
| MATH201.4 | Tutorial | Thursday 18:00 - 19:50 | B F2.6 | - | - |
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Monday | 12:00 - 14:00 | A F1.32 | |
| Tuesday | 11:00 - 12:00 | A F1.32 | |
| Wednesday | 13:00 - 14:00 | A F1.32 | |
| Thursday | 10:00 - 11:00 | A F1.32 |
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes :
Midterm Exam
AI: Not AllowedAlignment with Learning Outcomes :
Quizzes
AI: Not AllowedAlignment with Learning Outcomes :
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)
Active tutorials
26 hours ⏳ (13 week × 2 h)
Home study
52 hours ⏳ (13 week × 4 h)
In-term Exam Study
10 hours ⏳ (2 week × 5 h)
Midterm Exam Study
5 hours ⏳ (1 week × 5 h)
Final Exam Study
12 hours ⏳ (1 week × 12 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 [MATH201] 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 | |||||||||
| MATH201 | Linear Algebra | 3 | 2 | 6 | ||||||
| Prerequisite | MATH101 | It is a prerequisite to | CS404, CS405, CS414, CS415, EE439, IE303, MATH205 | |||||||
| Lecturer | Leila Miller | Office Hours / Room / Phone | Monday: 12:00-14:00 Tuesday: 11:00-12:00 Wednesday: 13:00-14:00 Thursday: 10:00-11:00 |
|||||||
| lmiller@ius.edu.ba | ||||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | The goal of the course is to explain the main concepts and fundamental processes of linear systems of algebraic equations and applications of the matrix theory to everyday problems. The course will provide all necessary material related linear algebra and after taking this course, students will realize linear algebra and linear transformations have impacts on our lives in many ways and will almost certainly become more important in future. | |||||||||
| Textbook | Linear Algebra, L. Miller, M. Can | |||||||||
| Additional Literature |
|
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| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
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| Teaching Methods | Class discussions with examples. Active tutorial sessions for engaged learning and continuous feedback on progress. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Linear Systems of Equations | 1.1, 1.2, 1.3 | ||||||||
| Week 2 | Linear Systems of Equations | 1.4 | ||||||||
| Week 3 | Vector Algebra | 2.1, 2.2, 2.3, 2.4, 2.5 | ||||||||
| Week 4 | Real Vector Spaces. FIRST IN-TERM EXAM | 3.1, 3.2, 3.3 | ||||||||
| Week 5 | Real Vector Spaces | 3.4, 3.5, 3.6 | ||||||||
| Week 6 | Inner Product Spaces | 4.1, 4.2 | ||||||||
| Week 7 | Inner Product Spaces MIDTERM EXAM | 4.3 | ||||||||
| Week 8 | Linear Transformations and Matrices | 5.1, 5.2 | ||||||||
| Week 9 | Linear Transformations and Matrices | 5.3 | ||||||||
| Week 10 | Eigenvalues and Eigenvectors SECOND IN-TERM EXAM | 6.1 | ||||||||
| Week 11 | Eigenvalues and Eigenvectors | 6.2 | ||||||||
| Week 12 | Eigenvalues and Eigenvectors | 6.3 | ||||||||
| Week 13 | Eigenvalues and Eigenvectors | 6.3 | ||||||||
| Week 14 | Applications | 7.1, 7.2, 7.3 | ||||||||
| Week 15 | Applications | 7.4, 7.5 | ||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 40 | Not Allowed | ||
| Semester Evaluation Components | |||||
| Midterm Exam | 1 | 30 | Not Allowed | ||
| Quizzes | 2 | 30 | Not Allowed | ||
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 3 | 15 | 45 | Active tutorials | 2 | 13 | 26 | |||
| Home study | 4 | 13 | 52 | In-term Exam Study | 5 | 2 | 10 | |||
| Midterm Exam Study | 5 | 1 | 5 | Final Exam Study | 12 | 1 | 12 | |||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 17/02/2026 | |||||||||