PATTERN RECOGNITION

Course Information
TitleΑΝΑΓΝΩΡΙΣΗ ΠΡΟΤΥΠΩΝ / PATTERN RECOGNITION
CodeNDM-06-03
FacultySciences
SchoolInformatics
Cycle / Level1st / Undergraduate, 2nd / Postgraduate
Teaching PeriodSpring
CoordinatorIoannis Pitas
CommonYes
StatusInactive
Course ID40002907

Programme of Study: PPS-Tmīma Plīroforikīs (2019-sīmera)

Registered students: 0
OrientationAttendance TypeSemesterYearECTS

Class Information
Academic Year2018 – 2019
Class PeriodSpring
Faculty Instructors
Weekly Hours4
Class ID
600121270
Course Type 2016-2020
  • Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
  • Face to face
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
  • English (Instruction, Examination)
Prerequisites
General Prerequisites
Basic knowledge in probability theory, programming skills, and good command of English.
Learning Outcomes
a) Knowledge: Familiarization with the fundamental principles, algorithms and technology of pattern recognition and statistical machine learning. Exposure to pattern recognition programming using C/C++ and MATLAB. Acquaintance with pattern recognition systems and their application in data analysis. b) Skills: Setting the foundations for advanced studies on pattern recognition issues and applications in data analysis, e.g. image analysis, shape recognition, signal analy-sis/recognition, analysis of medical information and signals/images, analysis of geographical data, analysis of web data, analysis of financial data.. Acquisition of skills in the use and development of pattern recognition algorithms. Promoting analytical and programming skills. Ability to develop basic pattern recognition applications using C/C++ and MATLAB.
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Adapt to new situations
  • Work autonomously
Course Content (Syllabus)
Random variables and vectors. Decision functions. Classification algorithms utilizing decision functions. Classification based on distance. Classification based on decision theory. Principal component analysis. Linear discriminant analysis. Estimation of probability distribution parameters. Analysis of similarity and web graphs. Syntactical pattern recognition. Vector quantization techniques. Programming assignments in C/C++ and MATLAB.
Keywords
Clustering, Classification, Statistical Machine Learning
Educational Material Types
  • Book
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures114
Project21
Exams15
Total150
Student Assessment
Description
Written exams. Literature survey or programming assignments or mid-term exams or oral presentations contribute another 2-4 grades, if the exam grading is at least 4.
Student Assessment methods
  • Written Exam with Short Answer Questions (Formative, Summative)
  • Written Assignment (Formative, Summative)
  • Performance / Staging (Formative, Summative)
  • Written Exam with Problem Solving (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
Sergios Theodoridis and Konstantinos Koutroumbas, Pattern Recognition, 2008. Μιχαήλ-Γεράσιμος Στρίντζης, «Αναγνώριση Προτύπων», Αφοί Κυριακίδη, Θεσσαλονίκη, 2007.
Additional bibliography for study
Keinosuke Fukunaga, "Introduction to Statistical Pattern Recognition", Academic Press, 1990. Richard O. Duda, Peter E. Hart and David G. Stork, Pattern Classification, 2000.
Last Update
03-06-2016