CS415 Pattern Recognition
CS415 Pattern Recognition
Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026
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Computer Sciences and Engineering
Course Objectives
The main objective of this course is to help students understand how we can learn from data. The course aims to introduce the algorithms that analyze data and is a fundamental course of that type for all students wishing to work in the field of machine learning. The tools introduced serve the purpose of learning how to work with data in general and understand the principles of analyzing data, as oppose to learning those particular tools. Some interesting topics include facial expression detection and intrusion detection using pattern recognition.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Pattern Recognition and Machine Learning, by Christopher Bishop, Springer 2006.
Additional Literature
Teaching Methods
This course employs a range of teaching and learning methods (lecturing, home assignments, working in lab, discussion, and project)
Students have three hours lectures and two hours practical training a week
Students are also expected to realise a project
Learning will consist of knowledge acquisition and practical use of that knowledge
Students’ independent learning and activities will greatly influence the achievement of learning outcomes
Consultations with staff should be used to its maximal potentials since individuals have different background and learning styles
Regular homework assignments will guide students’ individual learning and students’ progression in acquiring required knowledge and practice will be additionally checked through quizzes and midterm and final exams
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction to Pattern Recognition | Chapter 1 |
| 2 | Bayesian Decision Theory I | Chapter 2 |
| 3 | Bayesian Decision Theory II | Chapter 2 |
| 4 | Parameter Estimation Methods I | Chapter 3 |
| 5 | Parameter Estimation Methods II | Chapter 3 |
| 6 | Hidden Markov models for sequential pattern classification | Chapter 4 |
| 7 | Dimension reduction methods | Chapter 5 |
| 8 | Midterm preparation and MIDTERM EXAM | Chapters 1-5 |
| 9 | Non-parametric techniques for density estimation | Chapter 6 |
| 10 | Linear discriminate function based classifiers | Chapter 7 |
| 11 | Non-metric methods for pattern classification | Chapter 8 |
| 12 | Unsupervised learning and clustering | Chapter 9 |
| 13 | Unsupervised learning and clustering | Chapter 9 |
| 14 | Review | Chapters 1-9 |
| 15 | Review and final exam preparation |
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
Assignments / Labs
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Midterm Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Quizes
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Project
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
42 hours ⏳ (14 week × 3 h)
Mid-term exam study
15 hours ⏳ (1 week × 15 h)
Labs
42 hours ⏳ (14 week × 3 h)
Final exam study
12 hours ⏳ (1 week × 12 h)
Assignments
10 hours ⏳ (10 week × 1 h)
Project
24 hours ⏳ (8 week × 3 h)
Quizzes
15 hours ⏳ (3 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 [CS415] 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 | |||||||||
| CS415 | Pattern Recognition | 3 | 2 | 6 | ||||||
| Prerequisite | MATH201 | It is a prerequisite to | - | |||||||
| Lecturer | Office Hours / Room / Phone | Currently not available |
||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | The main objective of this course is to help students understand how we can learn from data. The course aims to introduce the algorithms that analyze data and is a fundamental course of that type for all students wishing to work in the field of machine learning. The tools introduced serve the purpose of learning how to work with data in general and understand the principles of analyzing data, as oppose to learning those particular tools. Some interesting topics include facial expression detection and intrusion detection using pattern recognition. |
|||||||||
| Textbook | Pattern Recognition and Machine Learning, by Christopher Bishop, Springer 2006. | |||||||||
| Additional Literature | ||||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | This course employs a range of teaching and learning methods (lecturing, home assignments, working in lab, discussion, and project). Students have three hours lectures and two hours practical training a week. Students are also expected to realise a project. Learning will consist of knowledge acquisition and practical use of that knowledge. Students’ independent learning and activities will greatly influence the achievement of learning outcomes. Consultations with staff should be used to its maximal potentials since individuals have different background and learning styles. Regular homework assignments will guide students’ individual learning and students’ progression in acquiring required knowledge and practice will be additionally checked through quizzes and midterm and final exams. | |||||||||
| Teaching Method Delivery | Teaching Method Delivery Notes | |||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction to Pattern Recognition | Chapter 1 | ||||||||
| Week 2 | Bayesian Decision Theory I | Chapter 2 | ||||||||
| Week 3 | Bayesian Decision Theory II | Chapter 2 | ||||||||
| Week 4 | Parameter Estimation Methods I | Chapter 3 | ||||||||
| Week 5 | Parameter Estimation Methods II | Chapter 3 | ||||||||
| Week 6 | Hidden Markov models for sequential pattern classification | Chapter 4 | ||||||||
| Week 7 | Dimension reduction methods | Chapter 5 | ||||||||
| Week 8 | Midterm preparation and MIDTERM EXAM | Chapters 1-5 | ||||||||
| Week 9 | Non-parametric techniques for density estimation | Chapter 6 | ||||||||
| Week 10 | Linear discriminate function based classifiers | Chapter 7 | ||||||||
| Week 11 | Non-metric methods for pattern classification | Chapter 8 | ||||||||
| Week 12 | Unsupervised learning and clustering | Chapter 9 | ||||||||
| Week 13 | Unsupervised learning and clustering | Chapter 9 | ||||||||
| Week 14 | Review | Chapters 1-9 | ||||||||
| Week 15 | Review and final exam preparation | |||||||||
| 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 | |||||
| Assignments / Labs | 10 | 10 | 1,2,3,4,5 | Not Allowed | |
| Midterm Exam | 1 | 25 | 1,2,3,4,5 | Not Allowed | |
| Quizes | 3 | 15 | 1,2,3,4,5 | Not Allowed | |
| Project | 1 | 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 | 14 | 42 | Mid-term exam study | 15 | 1 | 15 | |||
| Labs | 3 | 14 | 42 | Final exam study | 12 | 1 | 12 | |||
| Assignments | 1 | 10 | 10 | Project | 3 | 8 | 24 | |||
| Quizzes | 5 | 3 | 15 | |||||||
| Total Workload Hours = | 160 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 27/03/2026 | |||||||||