Pattern Recognition
| Title | ΑΝΑΓΝΩΡΙΣΗ ΠΡΟΤΥΠΩΝ / Pattern Recognition |
| Code | ΗΥ1401 |
| Faculty | Engineering |
| School | Electrical and Computer Engineering |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Winter |
| Coordinator | Panagiotis Petrantonakis |
| Common | No |
| Status | Active |
| Course ID | 20000591 |
| Academic Year | 2023 – 2024 |
| Class Period | Winter |
| Faculty Instructors | |
| Weekly Hours | 4 |
| Class ID | 600236110
|
Course Type 2016-2020
- Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600236110
- eLearning (Moodle): https://elearning.auth.gr/course/view.php?id=9013
Language of Instruction
- Greek (Instruction, Examination)
Prerequisites
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)
Keywords
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
Description
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
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 30 | ✓ | |||
| Laboratory Work | 24 | ✓ | |||
| Fieldwork | ✓ | ||||
| Tutorial | 6 | ||||
| Interactive Teaching in Information Center | 24 | ✓ | |||
| Project | 30 | ✓ | ✓ | ||
| Written assigments | 1 | ✓ | ✓ | ||
| Exams | 36 | ✓ | |||
| Total | 151 |
Student Assessment
Description
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)
Bibliography
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
Last Update
25-09-2023
