Dynamic Systems: Applications ton Signals, robotics and finance

Course Information
TitleΔυναμικά Συστήματα: Εφαρμογές σε σήματα, ρομποτική, οικονομία / Dynamic Systems: Applications ton Signals, robotics and finance
CodeDMCI107
FacultySciences
SchoolInformatics
Cycle / Level2nd / Postgraduate
Teaching PeriodWinter
CoordinatorKonstantinos(constantine) Kotropoulos
CommonNo
StatusActive
Course ID600016144

Programme of Study: PMS PSĪFIAKA MESA - YPOLOGISTIKĪ NOĪMOSYNĪ (2018 eōs sīmera) MF

Registered students: 0
OrientationAttendance TypeSemesterYearECTS
KORMOSElective Courses belonging to the selected specialization117.5

Programme of Study: PMS PSĪFIAKA MESA - YPOLOGISTIKĪ NOĪMOSYNĪ (2018 éōs sīmera) PF

Registered students: 0
OrientationAttendance TypeSemesterYearECTS
KORMOSElective Courses belonging to the selected specialization117.5
Class Information
Academic Year2018 – 2019
Class PeriodWinter
Faculty Instructors
Weekly Hours3
Class ID
600131998
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
  • Face to face
Digital Course Content
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
  • English (Instruction, Examination)
Prerequisites
General Prerequisites
Prior exposition to linear algebra, signals and systems, as well as stochastic signal processing facilitates to grasp faster the concepts introduced.
Learning Outcomes
1) Το get acquainted with computational models of estimation and prediction that are based on Bayesian inference, Kalman filters, particle filters, Gaussian filtering. 2) To apply the theory to dynamical systems, e.g., stock market, weather forecasting, recommndation in the web, robotics.
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 autonomously
  • Work in teams
  • Work in an interdisciplinary team
  • Generate new research ideas
  • Be critical and self-critical
  • Advance free, creative and causative thinking
Course Content (Syllabus)
Computational statistics. Dynamic systems and discrete-time Markov processes. Bayesian inference. Batch and recursive Bayesian estimation. Kalman filtering and its variations. Gaussian filtering. Data driven forecasting. Model driven forecasting and data assimilation. Applications to spatio-temporal processes (e.g., localization). Imperfect models.
Keywords
State equations, Bayesian inference, Kalman filtering
Educational Material Types
  • Slide presentations
  • Book
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Communication with Students
Description
Development of computer-based applications using MATLAB, python
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures39✓✓
Reading Assigment96✓✓
Project60✓✓✓
Written assigments15✓✓✓
Exams15✓✓
Total225
Student Assessment
Description
The written exams contribute to the final grade by 50%. Homework and projects contribute to the final grade by 40%. The active participation to the class lectures gives the remaining 10% of the final grade.
Student Assessment methods
  • Written Exam with Short Answer Questions (Formative, Summative)
  • Written Assignment (Formative, Summative)
  • Performance / Staging (Formative, Summative)
Bibliography
Additional bibliography for study
1. Steven M.Kay Fundamentals of Statistical Signal Processing, vol. I, Estimation Theory, Prentice Hall Signal Processing Series, Upper Saddle River, NJ: Prentice Hall, 1993. 2. James V. Candy, Bayesian Signal Processing: Classical, Modern, and Particle Filtering Methods, IEEE-Wiley, Hoboken, NJ: John Wiley and Sons, 2009. 3. C. K. Hui, and G. Chen, Kalman Filtering with Real-Time Applications, 3e. Berlin: Springer Verlag, 1999. 4. S. Reich and C. Cotter, Probabilistic Forecasting and Bayesian Data Assimilation, Cambridge, U.K.: Cambridge University Press, 2015.
Last Update
19-04-2019