Machine learning in biomedical data analysis
| Title | Μηχανική μάθηση στην ανάλυση βιοϊατρικών δεδομένων / Machine learning in biomedical data analysis |
| Code | ΒΜ020 |
| Faculty | Engineering |
| School | Electrical and Computer Engineering |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Spring |
| Coordinator | Panagiotis Petrantonakis |
| Common | No |
| Status | Active |
| Course ID | 600020762 |
Programme of Study: DPMS VIOÏATRIKĪ MĪCΗANIKĪ
Registered students: 11
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses | 2 | 1 | 5 |
| Academic Year | 2023 – 2024 |
| Class Period | Spring |
| Faculty Instructors |
|
| Weekly Hours | 4 |
| Class ID | 600245940
|
Course Type 2021
Specific Foundation
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600245940
- eLearning: https://elearning.auth.gr/course/view.php?id=17579
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
- English (Instruction, Examination)
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Make decisions
- Work autonomously
- Work in an interdisciplinary team
- Be critical and self-critical
- Advance free, creative and causative thinking
Course Content (Syllabus)
In the frame of the course technical introduction is given to pattern classification and identification, with emphasis on biomedical data. The particular classical and modern techniques of machine learning is studied, and the areas and approaches of applications is presented. Topics, such as the quantification and diagnosis of disease as well as patient classification is combined with structural data analysis with methods including nonlinear and connectivity analysis and complex networks, as well as unstructured data and text analysis and image analysis. In terms of methodology, machine learning techniques for classification and regression are presented (e.g., linear classification and regression, support vector machines, manifold learning as well as ensemble learning, such as random forests, bagging and boosting), including dimension reduction techniques. Projects are given in the application of machine learning techniques in clinical practice. The course focuses on the understanding and application of machine learning techniques commonly used in biomedical applications.
Educational Material Types
- Notes
- Slide presentations
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | |||||
| Laboratory Work | |||||
| Total |
Student Assessment
Description
Project with Oral Presentation
Student Assessment methods
- Performance / Staging (Formative, Summative)
- Written Exam with Problem Solving (Formative, Summative)
- Report (Formative, Summative)
Bibliography
Additional bibliography for study
Bishop, Christopher M., and Nasser M. Nasrabadi. Pattern recognition and machine learning. Vol. 4. No. 4. New York: springer, 2006.
- Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016. (Available Online: https://www.deeplearningbook.org/)
- Dey, Nilanjan, et al., eds. Machine learning in bio-signal analysis and diagnostic imaging. Academic Press, 2018.
Last Update
18-12-2023
