Biomedical Data and Machine Learning
| Title | Βιοϊατρικά Δεδομένα και Μηχανική Μάθηση / Biomedical Data and Machine Learning |
| Code | MEI003 |
| Faculty | Health Sciences |
| School | Medicine |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter |
| Coordinator | Ioanna Chouvarda |
| Common | No |
| Status | Active |
| Course ID | 600021929 |
Programme of Study: PMS "Iatrikī Mīchanikī kai Plīroforikī" (2022-sīmera)
Registered students: 9
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Compulsory Course | 1 | 1 | 8 |
| Academic Year | 2024 – 2025 |
| Class Period | Winter |
| Faculty Instructors |
|
| Instructors from Other Categories |
|
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600265083
|
Course Type 2021
General Foundation
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600265083
- eLearning: https://elearning.auth.gr/course/view.php?id=17076
Language of Instruction
- English (Instruction, Examination)
Prerequisites
General Prerequisites
basic programming
Learning Outcomes
Aims
1 Understanding of the basic challenges of data science in medicine
2 Understanding of machine learning methods
3 Familiarization with the use of ML techniques and tools
4 Familiarization with the application of ML methods towards solving specific problems with different types of biomedical data
Expected outcomes
1 Understand the concepts, theory and terminology around Machine Learning topics
2 Understand the basic methods of biomedical data analysis and machine learning in medical problems
3 To apply and make use of ML technologies in problems originating from the medical practice
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Work autonomously
- Work in teams
- Be critical and self-critical
Course Content (Syllabus)
Introduction, basic concepts, history and perspectives
Data science in biomedicine - data types, structure, quality
Descriptive Analysis , feature extraction, Selection and dimensionality
Descriptive Analysis lab
Machine learning models - supervised
Machine learning models - unsupervised
ML Lab
Deep learning introduction
Deep Learning Lab
Trust, Fairness and Explainability
Applications and hands on
Keywords
machine learning, biomedical data analysis
Educational Material Types
- Slide presentations
- Video lectures
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Description
The nature of the lesson is heavily related with the information and communication technologies
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 60 | 2.4 | ✓ | ||
| Seminars | 4 | 0.2 | ✓ | ||
| Laboratory Work | 20 | 0.8 | ✓ | ✓ | |
| Reading Assigment | 6 | 0.2 | |||
| Project | 60 | 2.4 | ✓ | ✓ | |
| Written assigments | 30 | 1.2 | ✓ | ✓ | |
| Exams | 20 | 0.8 | |||
| Total | 200 | 8 |
Student Assessment
Description
projects
quiz on theory
quiz after handson
Student Assessment methods
- Written Exam with Multiple Choice Questions (Summative)
- Written Assignment (Summative)
- Oral Exams (Summative)
- Report (Summative)
- Labortatory Assignment (Formative)
Last Update
04-10-2023
