Pattern Recognition

Course Information
TitleΑΝΑΓΝΩΡΙΣΗ ΠΡΟΤΥΠΩΝ / Pattern Recognition
SchoolElectrical and Computer Engineering
Cycle / Level1st / Undergraduate
Teaching PeriodWinter
CoordinatorPanagiotis Petrantonakis
Course ID20000591

Class Information
Academic Year2017 – 2018
Class PeriodWinter
Faculty Instructors
Weekly Hours4
Class ID
Course Type 2016-2020
  • Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
  • Face to face
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
  • English (Instruction, Examination)
Required Courses
  • ΜΑ0301 Probability and Statistics
Learning Outcomes
By the end of the course students are expected to: a) Know the basic principles of pattern recognition theory and the main application domains b) Understand the fundamental pattern recognition methods and algorithms c) Apply well-known algorithms to pilot problems d) Select the most efficient algorithm, based on problem requirements e) Design the methodology for pattern recognition problems of medium complexity
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Make decisions
  • Work in teams
  • Advance free, creative and causative thinking
Course Content (Syllabus)
Pattern recognition focuses on the design of systems capable of extracting patterns from data. Issues related to feature extraction, error estimation and model statistics are also discussed. State-of-the art fields such as Machine learning, Data mining, Computer vision and Human Computer Interaction emloy Pattern Recognition principles. Topics covered within the context of the course: a) Introduction to Pattern Recognition principles b) Data preprocessing and exploration c) Classification: Basic principles, Model evaluation, Bayesian algorithms, Decision trees, SVMs (linear/non-linear), Perceptrons, Classifier Ensembles d) Clustering: Basic principles, Model evaluation, Partitioning algorithms, Hierarchical algorithms, Density-based algorithms, SOMs, Mixture models e) Data reduction (PCA, ISOMAP)
Data Analysis, Classification, Clustering, Pattern recognition
Educational Material Types
  • Notes
  • Slide presentations
  • Interactive excersises
  • Book
  • 1 project
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
  • Use of ICT in Student Assessment
eLearning, a Moodle-based system is offered by the AUTH IT team and is customized to the needs of the ECE courses. eLearning allows instructors to post announcements, communicate with students, upload lectures, exercises and their solutions, set up and run course projects, while it also offers self-assessment capabilities. eLearning also supports a Forum for coursework discussion. Additionally, the eLearning platform is used for online quizzes and exams. Finally, courses exercises are conducted at the ECE computer labs with the help of state-of-the-art pattern recognition suites.
Course Organization
Laboratory Work24
Interactive Teaching in Information Center24
Written assigments1
Student Assessment
During the semester an optional assignment on state-of-the-art classification or clustering problems is provided, to be conducted in teams of 2 students. The final course grade is related to the performance of students in two online quizzes: Quiz 1 (Classification) - 45% of the final grade Quiz 2 (Clustering) - 55% of the final grade
Student Assessment methods
  • Written Exam with Multiple Choice Questions (Formative, Summative)
  • Written Exam with Short Answer Questions (Formative, Summative)
  • Written Assignment (Summative)
  • Oral Exams (Formative)
  • Written Exam with Problem Solving (Formative)
  • Report (Formative)
Course Bibliography (Eudoxus)
1. “Εισαγωγή στην εξόρυξη δεδομένων”, P.N. Tan, M. Steinbach, V. Kumar, Εκδόσεις: Α. Τζιόλλα & υιοί Α.Ε., 2010, ISBN: 978-960-418-162-9, Κωδικός Βιβλίου στον Εύδοξο: 18549105. 2. “Αναγνώριση Προτύπων”, Σ. Θεοδωρίδης, Κ. Κουτρουμπάς, Εκδόσεις Πασχαλίδη, 2011, ISBN: 9789604891450, Κωδικός Βιβλίου στον Εύδοξο: 13256974. 3. “Αναγνώριση Προτύπων”, Μ. Γ. Στρίντζης, Εκδόσεις Αφοί Κυριακίδη, 2000, ISBN: 978-960-343-290-6, Κωδικός Βιβλίου στον Εύδοξο: 6378.
Additional bibliography for study
Τίτλος Συγγράμματος: «Εισαγωγή στην αναγνώριση προτύπων με Matlab» Συγγραφέας: THEODORIDIS S., PIKRAKIS A., KOUTROUMBAS K., CAVOURAS D. Εκδόσεις: 2011 Πασχαλίδης ή BROKEN HILL PUBLISHERS LTD ISBN: 9789604890231 Κωδικός Βιβλίου στον Εύδοξο: 13256624 Τίτλος Συγγράμματος: «Αναγνώριση Προτύπων», Συγγραφέας: Μ. Στρίντζης Εκδόσεις: Κυριακίδη 2007 ISBN: 978-960-343-290-6 Κωδικός Βιβλίου στον Εύδοξο: 6378
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