SIGNAL ANALYSIS AND HIDEN MARKOV MODELS
| Title | ΑΝΑΛΥΣΗ ΣΗΜΑΤΩΝ & ΚΡΥΦΑ ΜΑΡΚΟΒΙΑΝΑ ΜΟΝΤΕΛΑ / SIGNAL ANALYSIS AND HIDEN MARKOV MODELS |
| Code | ΣΜΥ001 |
| Faculty | Sciences |
| School | Mathematics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter/Spring |
| Coordinator | Alexandra Papadopoulou |
| Common | Yes |
| Status | Active |
| Course ID | 600025925 |
Programme of Study: PMS Tmīmatos Mathīmatikṓn (2025-2030)
Registered students: 0
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| STATISTIKĪ, MONTELOPOIĪSĪ KAI YPOLOGISTIKES METHODOI | Elective Courses belonging to the selected specialization | Winter | - | 10 |
| Academic Year | 2025 – 2026 |
| Class Period | Winter |
| Faculty Instructors | |
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600268393
|
Type Of Offer
- Disciplinary Course
Course Type 2021
Specialization / Direction
Mode of Delivery
- Face to face
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600268393
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
Prerequisites
General Prerequisites
Elements of probability theory, stochastic processes, calculus and linear algebra.
Learning Outcomes
Upon successful completion of the course, students will:
1. understand the basic principles of hidden Markov models.
2. be able to solve stochastic problems using theoretical tools.
3. practice in stochastic modelling.
General Competences
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Make decisions
- Work autonomously
- Work in teams
- Generate new research ideas
- Be critical and self-critical
- Advance free, creative and causative thinking
Course Content (Syllabus)
Hidden Markov Models.
Educational Material Types
- Notes
- Slide presentations
- Multimedia
- Book
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
Description
Implementation of the methods with the R programming language.
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | ||||
| Reading Assigment | 183 | ||||
| Tutorial | 13 | ||||
| Project | 20 | ||||
| Written assigments | 40 | ||||
| Exams | 5 | ||||
| Total | 300 |
Student Assessment
Description
The final grade for this course will be calculated as follows:
1. Midterm: 40%
2. Final exam: 60%.
Student Assessment methods
- Written Exam with Short Answer Questions (Formative, Summative)
- Written Exam with Extended Answer Questions (Formative, Summative)
- Written Assignment (Formative, Summative)
- Performance / Staging (Formative, Summative)
Bibliography
Additional bibliography for study
Robert J. Elliott , John B. Moore , Lakhdar Aggoun, "Hidden Markov Models: Estimation and Control", 1995.
Last Update
16-05-2025
