EE439 Optimal Filtering
EE439 Optimal Filtering
Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026
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Electrical and Electronics Engineering
Course Objectives
The aims of this course are to: - give students an understanding of the fundamental principles of Optimal Filtering, and their applications to everyday life and technology - provide a reasonably broad perspective of Optimal Filtering thus developing an understanding of the models of the physical environment and of how human beings - provide a complete idea of Optimal Filtering transient and steady state behavior, as well as stability, for the use of them in other engineering courses, as well as further graduate studies in engineering interact with it - develop an understanding of the power of Optimal Filtering through various techniques and applications - Be able to analyze Optimal Filtering algorithms used in engineering and science and develop an ability of modelling physical systems using methods of Optimal Filtering
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Anderson, B. D., & Moore, J. B. (2005). Optimal filtering. Mineola, NY: Dover Publications.
Additional Literature
Simon, D. (2006). Optimal state estimation: Kalman, H and nonlinear approaches. New Jersey: Wiley.Teaching Methods
Lectures with presentations
Tutorials and laboratories by solving exercises
Discussions
Weekly Topics
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Course Schedule (All Sections)
Office Hours & Room
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Midterm
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Assignments
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Quizzes
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Labs
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)
Tutorial / Practical training
30 hours ⏳ (15 week × 2 h)
Individual learning
75 hours ⏳ (15 week × 5 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 [EE439] 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 | |||||||||
| EE439 | Optimal Filtering | 3 | 2 | 6 | ||||||
| Prerequisite | MATH201 | It is a prerequisite to | - | |||||||
| Lecturer | Office Hours / Room / Phone | Currently not available |
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| Assistant | Assistant E-mail | |||||||||
| Course Objectives | The aims of this course are to: - give students an understanding of the fundamental principles of Optimal Filtering, and their applications to everyday life and technology - provide a reasonably broad perspective of Optimal Filtering thus developing an understanding of the models of the physical environment and of how human beings - provide a complete idea of Optimal Filtering transient and steady state behavior, as well as stability, for the use of them in other engineering courses, as well as further graduate studies in engineering interact with it - develop an understanding of the power of Optimal Filtering through various techniques and applications - Be able to analyze Optimal Filtering algorithms used in engineering and science and develop an ability of modelling physical systems using methods of Optimal Filtering |
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| Textbook | Anderson, B. D., & Moore, J. B. (2005). Optimal filtering. Mineola, NY: Dover Publications. | |||||||||
| 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 | Lectures with presentations, tutorials and laboratories by solving exercises, discussions | |||||||||
| Teaching Method Delivery | Teaching Method Delivery Notes | |||||||||
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| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 40 | 1,2,3,4,5 | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm | 1 | 30 | 1,2,3,4,5 | Not Allowed | |
| Assignments | 1 | 20 | 1,2,3,4,5 | Not Allowed | |
| Quizzes | 1 | 10 | 1,2,3,4,5 | Not Allowed | |
| Labs | 1 | 5 | 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 | Tutorial / Practical training | 2 | 15 | 30 | |||
| Individual learning | 5 | 15 | 75 | 1 | 0 | |||||
| 0 | 0 | |||||||||
| 0 | 0 | |||||||||
| 0 | 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 | |||||||||