CS417 Introduction to Data Mining
Syllabus | International University of Sarajevo - Last Update on Feb 02, 2026
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Computer Sciences and Engineering
Emine Yaman
Course Lecturer
Course Objectives
The aims of this course are to presents to students well-known data mining techniques and their application areas. Specifically, the course demonstrates basic concepts, principals and methods of data mining. It also demonstrates the process of Knowledge Discovery in Databases (KDD) and presents a review of available tools.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Introduction to Data Mining, Pang Ning Tan, Michael Steinbach, Vipin Kumar, Pearson, 2005.
Additional Literature
Data Mining: The Textbook, Charu C. Aggarwal Hardcover, Springer.Teaching Methods
Class discussions with examples
Active tutorial sessions for engaged learning and continuous feedback on progress
Home assigments
Projects that involve a data mining application from real life
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction to Course, Introduction to Data Mining | Chapter 1 |
| 2 | Data: Attributes and Objects, Types of Data, Data Quality, Similarity and Distance, Data Preprocessing Methods. | Chapter 2 |
| 3 | Peason's Correlation, Spearman's Rank Correlation, Kendal's Tau, Proximity Measures. | Chapter 2 |
| 4 | Basic Classification: Classification Techniques, Measures of Node Impurity (Gini, Entropy, Misclassification Error) | Chapter 3 |
| 5 | Overfitting: Classification Errors, Model Underfitting and Overfitting, Model Selection, Complexity, Model Evaluation | Chapter 3 |
| 6 | Rule-Based Classifier: Applications, Rule Coverage and Accuracy, Rule Ordering Schemas, Direct/Indirect Methods. KNN: Architecture, Examples, Accuaracy. | Chapter 4 |
| 7 | Bayes Classifier: Architecture, Examples, Accuaracy. Artificial Neural Networks: Architecture, Examples, Accuaracy. | Chapter 4 |
| 8 | MIDTERM EXAM | |
| 9 | Imbalanced Class Problem: Confusion Matrix, Accuracy, Receiver Operating Characteristic. | Chapter 4 |
| 10 | Association Analysis-Basic Concepts: Association Rule Mining, Appriori Algorithm/Principles, Architecture, Examples, Complexity, Pattern Evaluation. | Chapter 5 |
| 11 | Association Analysis-Advanced Concepts: Extension of Association Analysis to Continues and Categorical Attributes and Multilevel Rules, Sequential Patterns, Subgraph Mining. | Chapter 6 |
| 12 | Cluster Analysis-Basic Concepts: Types of Clustering, Clustering Algorithms (k-means, hierarchical, density based) | Chapter 7 |
| 13 | Cluster Analysis-Advanced Concepts: Advanced Algorithms (prototype-based, graph-based), Characteristics of Clustering Algorithms. | Chapter 8 |
| 14 | Anomaly Detection: Anomaly Detection Techniques (statistical approach, proximity-based, clustering-based, reconstruction-based), Evaluation of Anomaly Detection. | Chapter 9 |
| 15 | Presentation of Projects |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| CS417.1 | Course | Thursday 09:00 - 11:50 | B F1.9 | - | - |
| CS417.1 | Tutorial | Friday 14:00 - 15:50 | A F1.10 | - | - |
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Wednesday | 10:00 - 12:00 | A F1.34 | |
| Thursday | 10:00 - 12:00 | A F1.34 | |
| Friday | 10:00 - 12:00 | A F1.34 |
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Midterm Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4
Homeworks
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Term Project/Presentation
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)
Homeworks
10 hours ⏳ (5 week × 2 h)
Home Study
30 hours ⏳ (15 week × 2 h)
Midterm Exam Preparation
10 hours ⏳ (1 week × 10 h)
Final Exam Preparation
14 hours ⏳ (1 week × 14 h)
Term Project/Presentation
15 hours ⏳ (1 week × 15 h)
Labs
20 hours ⏳ (10 week × 2 h)
Quizzes Preparation
6 hours ⏳ (2 week × 3 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 [CS417] 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 | |||||||||
| CS417 | Introduction to Data Mining | 3 | 2 | 6 | Thursday 9:00-11:50 | |||||
| Prerequisite | CS206 | It is a prerequisite to | - | |||||||
| Lecturer | Emine Yaman | Office Hours / Room / Phone | Wednesday: 10:00-12:00 Thursday: 10:00-12:00 Friday: 10:00-12:00 |
|||||||
| eyaman@ius.edu.ba | ||||||||||
| Assistant | Harun Hadžo | Assistant E-mail | hhadzo@ius.edu.ba | |||||||
| Course Objectives | The aims of this course are to presents to students well-known data mining techniques and their application areas. Specifically, the course demonstrates basic concepts, principals and methods of data mining. It also demonstrates the process of Knowledge Discovery in Databases (KDD) and presents a review of available tools. |
|||||||||
| Textbook | Introduction to Data Mining, Pang Ning Tan, Michael Steinbach, Vipin Kumar, Pearson, 2005. | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | Class discussions with examples. Active tutorial sessions for engaged learning and continuous feedback on progress. Home assigments. Projects that involve a data mining application from real life. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction to Course, Introduction to Data Mining | Chapter 1 | ||||||||
| Week 2 | Data: Attributes and Objects, Types of Data, Data Quality, Similarity and Distance, Data Preprocessing Methods. | Chapter 2 | ||||||||
| Week 3 | Peason's Correlation, Spearman's Rank Correlation, Kendal's Tau, Proximity Measures. | Chapter 2 | ||||||||
| Week 4 | Basic Classification: Classification Techniques, Measures of Node Impurity (Gini, Entropy, Misclassification Error) | Chapter 3 | ||||||||
| Week 5 | Overfitting: Classification Errors, Model Underfitting and Overfitting, Model Selection, Complexity, Model Evaluation | Chapter 3 | ||||||||
| Week 6 | Rule-Based Classifier: Applications, Rule Coverage and Accuracy, Rule Ordering Schemas, Direct/Indirect Methods. KNN: Architecture, Examples, Accuaracy. | Chapter 4 | ||||||||
| Week 7 | Bayes Classifier: Architecture, Examples, Accuaracy. Artificial Neural Networks: Architecture, Examples, Accuaracy. | Chapter 4 | ||||||||
| Week 8 | MIDTERM EXAM | |||||||||
| Week 9 | Imbalanced Class Problem: Confusion Matrix, Accuracy, Receiver Operating Characteristic. | Chapter 4 | ||||||||
| Week 10 | Association Analysis-Basic Concepts: Association Rule Mining, Appriori Algorithm/Principles, Architecture, Examples, Complexity, Pattern Evaluation. | Chapter 5 | ||||||||
| Week 11 | Association Analysis-Advanced Concepts: Extension of Association Analysis to Continues and Categorical Attributes and Multilevel Rules, Sequential Patterns, Subgraph Mining. | Chapter 6 | ||||||||
| Week 12 | Cluster Analysis-Basic Concepts: Types of Clustering, Clustering Algorithms (k-means, hierarchical, density based) | Chapter 7 | ||||||||
| Week 13 | Cluster Analysis-Advanced Concepts: Advanced Algorithms (prototype-based, graph-based), Characteristics of Clustering Algorithms. | Chapter 8 | ||||||||
| Week 14 | Anomaly Detection: Anomaly Detection Techniques (statistical approach, proximity-based, clustering-based, reconstruction-based), Evaluation of Anomaly Detection. | Chapter 9 | ||||||||
| Week 15 | Presentation of Projects | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 35 | 1,2,3,4,5 | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm Exam | 1 | 25 | 1,2,3,4 | Not Allowed | |
| Homeworks | 5 | 5 | 1,2,3,4,5 | Not Allowed | |
| Term Project/Presentation | 1 | 10 | 1,2,3,4,5 | Not Allowed | |
| Quizzes | 2 | 15 | 1,2,3,4,5 | Not Allowed | |
| Labs | 10 | 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 | 15 | 45 | Homeworks | 2 | 5 | 10 | |||
| Home Study | 2 | 15 | 30 | Midterm Exam Preparation | 10 | 1 | 10 | |||
| Final Exam Preparation | 14 | 1 | 14 | Term Project/Presentation | 15 | 1 | 15 | |||
| Labs | 2 | 10 | 20 | Quizzes Preparation | 3 | 2 | 6 | |||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 25/02/2026 | |||||||||