CS512 Machine Learning in Medicine and Health
CS512 Machine Learning in Medicine and Health
Syllabus | International University of Sarajevo - Last Update on Oct 10, 2025
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Computer Sciences and Engineering
Kanita Karađuzović-Hadžiabdić
Course Lecturer
Course Objectives
"• Introduce/understand the basic principles of machine learning • Understand and manage the data • Select and apply machine learning methods in medicine and healthcare • Performance evaluation of machine learning models applied to (bio)medical data • Critically read relevant research articles on machine learning in (bio)medical data"
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
1.Machine Learning with R, Brett Lantz, Packt Publishing, 2013l 2. Introduction to Data Mining, Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Addison-Wesley, 2006 2. Supplementary Books: 1. Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer, 2006; 2. Introduction to Statistical Learning, with Applications in Python by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, and Jonathan Taylor. , Springer 2023 ;
Additional Literature
Class Notes, Relevant PapersTeaching Methods
The student is expected to carry out a semester long project in order to demonstrate the skills required to implement a real-world project
Work on the project is expected to start early in the semester and to be presented at the end of the semester
Three hour lectures will include necessary theoretical knowledge backed up with practical coding sessions using real-world data
Lectures will also include regular project discussions in order to guide students for the term project
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction to the course, getting started with Machine Learning. | Chapter 1, Intro to Data Mining; Chapter 1 ML in R |
| 2 | Understanding and preparing data, K-nearest neighbour algorithm, Evaluating Model Performance | KNN: 3.1-3.4, 3.6, 3.7, Intro to Data Mining; Chapter 3, Machine Learning in R; |
| 3 | Decision Trees, Ensemble methods, Feature Selection | Chapter 3.3, 6.2, 6.11, Intro to Data Mining; Chapter 5, 10, Machine Learning in R; ENSEMBLE METHODS: Chapter 6.6, Intro to Data Mining; Chapter 6, Machine Learning in R |
| 4 | Regression | Chapter 6.7, Intro to Data Mining; Chapter 6 ML in R |
| 5 | Naïve Bayes Alg. | Chapter 4, both books |
| 6 | Paper discussion/presentations | |
| 7 | Artificial neural networks | Chapter 6.7, Intro to Data Mining; Chapter 7 ML in R |
| 8 | Project Pitch | |
| 9 | Support Vector Machines | Chapter 6.9, Intro to Data Mining; Chapter 7 ML in R |
| 10 | Clustering | Chapter 5, Intro to Data Mining; Chapter 9 ML in R |
| 11 | Paper discussion/presentations | |
| 12 | Project consultations | |
| 13 | Project presentations | |
| 14 | Project presentations | |
| 15 | Review for the Final Exam |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| CS512.1 | Course | Wednesday 17:00 - 19:50 | A F1.4 - Class/Laboratory | - | - |
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Tuesday | 10:00 - 12:00 | B F3.13 | |
| Wednesday | 14:00 - 17:00 | B F3.13 |
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Paper Presentation
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5 6 7 8
Project
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5 6 7 8
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)
Home study
42 hours ⏳ (14 week × 3 h)
Project
30 hours ⏳ (6 week × 5 h)
Final exam study
9 hours ⏳ (1 week × 9 h)
Preperation for paper presentation
24 hours ⏳ (6 week × 4 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 [CS512] 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 Oct 10, 2025 | International University of Sarajevo
Print Syllabus
Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| CS512 | Machine Learning in Medicine and Health | 3 | 0 | 6 | ||||||
| Prerequisite | None | It is a prerequisite to | - | |||||||
| Lecturer | Kanita Karađuzović-Hadžiabdić | Office Hours / Room / Phone | Tuesday: 10:00-12:00 Wednesday: 14:00-17:00 |
|||||||
| kanita@ius.edu.ba | ||||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | "• Introduce/understand the basic principles of machine learning • Understand and manage the data • Select and apply machine learning methods in medicine and healthcare • Performance evaluation of machine learning models applied to (bio)medical data • Critically read relevant research articles on machine learning in (bio)medical data" |
|||||||||
| Textbook | 1.Machine Learning with R, Brett Lantz, Packt Publishing, 2013l 2. Introduction to Data Mining, Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Addison-Wesley, 2006 2. Supplementary Books: 1. Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer, 2006; 2. Introduction to Statistical Learning, with Applications in Python by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, and Jonathan Taylor. , Springer 2023 ; | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | The student is expected to carry out a semester long project in order to demonstrate the skills required to implement a real-world project. Work on the project is expected to start early in the semester and to be presented at the end of the semester. Three hour lectures will include necessary theoretical knowledge backed up with practical coding sessions using real-world data. Lectures will also include regular project discussions in order to guide students for the term project. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction to the course, getting started with Machine Learning. | Chapter 1, Intro to Data Mining; Chapter 1 ML in R | ||||||||
| Week 2 | Understanding and preparing data, K-nearest neighbour algorithm, Evaluating Model Performance | KNN: 3.1-3.4, 3.6, 3.7, Intro to Data Mining; Chapter 3, Machine Learning in R; | ||||||||
| Week 3 | Decision Trees, Ensemble methods, Feature Selection | Chapter 3.3, 6.2, 6.11, Intro to Data Mining; Chapter 5, 10, Machine Learning in R; ENSEMBLE METHODS: Chapter 6.6, Intro to Data Mining; Chapter 6, Machine Learning in R | ||||||||
| Week 4 | Regression | Chapter 6.7, Intro to Data Mining; Chapter 6 ML in R | ||||||||
| Week 5 | Naïve Bayes Alg. | Chapter 4, both books | ||||||||
| Week 6 | Paper discussion/presentations | |||||||||
| Week 7 | Artificial neural networks | Chapter 6.7, Intro to Data Mining; Chapter 7 ML in R | ||||||||
| Week 8 | Project Pitch | |||||||||
| Week 9 | Support Vector Machines | Chapter 6.9, Intro to Data Mining; Chapter 7 ML in R | ||||||||
| Week 10 | Clustering | Chapter 5, Intro to Data Mining; Chapter 9 ML in R | ||||||||
| Week 11 | Paper discussion/presentations | |||||||||
| Week 12 | Project consultations | |||||||||
| Week 13 | Project presentations | |||||||||
| Week 14 | Project presentations | |||||||||
| Week 15 | Review for the Final Exam | |||||||||
| 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 | |||||
| Paper Presentation | 1 | 20 | 1-8 | Not Allowed | |
| Project | 1 | 45 | 1-8 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 3 | 15 | 45 | Home study | 3 | 14 | 42 | |||
| Project | 5 | 6 | 30 | Final exam study | 9 | 1 | 9 | |||
| Preperation for paper presentation | 4 | 6 | 24 | 0 | ||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 01/10/2025 | |||||||||