Data analytics and machine learning
| Title | Ανάλυση δεδομένων και μηχανική μάθηση / Data analytics and machine learning |
| Code | OD11 |
| Faculty | Health Sciences |
| School | Medicine |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter/Spring |
| Coordinator | Panagiotis Bamidis |
| Common | No |
| Status | Active |
| Course ID | 600024792 |
Programme of Study: Joint PPS "Managing Digital Transformation in the Health Sector" (2024-today)
Registered students: 38
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| Data Science | EPILEGOMENA EIDIKEUSĪS | 2 | 1 | 6 |
| Academic Year | 2024 – 2025 |
| Class Period | Spring |
| Faculty Instructors | |
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600256607
|
Course Type 2021
Specialization / Direction
Mode of Delivery
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600256607
- eLearning (Moodle): https://elearning.digihealthedu.eu/course/view.php?id=7
Language of Instruction
- English (Instruction, Examination)
Learning Outcomes
After this course, students will be able to:
Apply and understand various stages within the realm of machine learning.
Recognize and locate crucial data points.
Employ imputation techniques for data replacement and establish appropriate metrics.
Identify and employ supervised and unsupervised algorithms suitable for health data analysis.
Evaluate and interpret the performance of the various machine-learning algorithms on health data.
Implement a machine learning pipeline in an ML toolkit.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Make decisions
- Work in teams
- Work in an international context
- Work in an interdisciplinary team
- Respect natural environment
- Advance free, creative and causative thinking
Course Content (Syllabus)
Foundations of AI in Health
Setting up the working Environment
Data Pre-processing
Machine Learning Algorithms
Applications
Educational Material Types
- Notes
- Slide presentations
- Video lectures
- Podcast
- Multimedia
- Interactive excersises
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | 1.6 | ✓ | ||
| Reading Assigment | 21 | 0.8 | ✓ | ||
| Project | 20 | 0.8 | ✓ | ||
| Written assigments | 70 | 2.8 | ✓ | ||
| Total | 150 | 6 |
Student Assessment
Student Assessment methods
- Written Assignment (Formative, Summative)
- Report (Formative, Summative)
Last Update
10-07-2024
