PATTERN RECOGNITION-STATISTICAL LEARNING

Course Information
TitleΑΝΑΓΝΩΡΙΣΗ ΠΡΟΤΥΠΩΝ - ΣΤΑΤΙΣΤΙΚΗ ΜΑΘΗΣΗ / PATTERN RECOGNITION-STATISTICAL LEARNING
CodeNDM-06-04
FacultySciences
SchoolInformatics
Cycle / Level1st / Undergraduate, 2nd / Postgraduate
Teaching PeriodSpring
CoordinatorIoannis Pitas
CommonNo
StatusActive
Course ID600016577

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

Registered students: 44
OrientationAttendance TypeSemesterYearECTS
GENIKĪ KATEUTHYNSĪYPOCΗREŌTIKO KATA EPILOGĪ635

Class Information
Academic Year2019 – 2020
Class PeriodSpring
Faculty Instructors
Weekly Hours4
Class ID
600155621
Course Type 2016-2020
  • Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
  • Face to face
Digital Course Content
Language of Instruction
  • Greek (Instruction, Examination)
Prerequisites
General Prerequisites
Basic knowledge in probability theory. Programming skills. Good level 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 Python, C/C++ and MATLAB. Acquaintance with pattern recognition and statistical machine learning 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, face recognition, action recognition, signal analysis/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 Python, C/C++ and MATLAB.
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 teams
  • Work in an international context
  • Work in an interdisciplinary team
  • Generate new research ideas
  • Design and manage projects
  • Be critical and self-critical
  • Advance free, creative and causative thinking
Course Content (Syllabus)
Clustering (unsupervised learning). Classification/recognition (supervised learning). Decision functions. Classification algorithms utilizing decision functions. Classification based on distance. Classification based on decision theory. Estimation of probability distribution parameters. Semi-supervised learning. Dimensionality reduction (Principal component analysis, Linear discriminant analysis). Graph-based pattern recognition (Analysis of similarity and web graphs, Spectral clustering). Fuzzy logic and pattern recognition. Gaussian Mixture Models. Bayesian networks. Programming assignments in Python, C/C++ and MATLAB.
Keywords
Random variables, decision functions, clustering, classification based on distance, classification based on decision theory, principal component analysis, linear discriminant analysis, estimation of probability distribution parameters, graph-based pattern recognition, analysis of similarity and web graphs, spectral clustering, fuzzy logic, vector quantization, Bayesian networks, semi-supervised learning.
Educational Material Types
  • Book
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
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures104
Reading Assigment21
Project25
Total150
Student Assessment
Description
Written exams. Bibliography or programming assignments or proodos or oral presentations provide an additional 2-4 points, if the exam mark is at least 4.
Student Assessment methods
  • Written Exam with Problem Solving (Formative, Summative)
  • Report (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
1) Sergios Theodoridis and Konstantinos Koutroumbas, Pattern Recognition, 2008 2) Στρίντζης Μ. «Αναγνώριση Προτύπων», Αφοί Κυριακίδη, Θεσσαλονίκη, 2007.
Additional bibliography for study
1) C. Bishop, Pattern Recognition and Machine Learning, Springer 2011 2) Richard O. Duda, Peter E. Hart and David G. Stork, Pattern Classification, Wiley 2007
Last Update
09-12-2020