ECTS - Pattern Classification and Sensor Applications for Engineers

Pattern Classification and Sensor Applications for Engineers (EE449) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Pattern Classification and Sensor Applications for Engineers EE449 Area Elective 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type Elective Courses
Course Level Bachelor’s Degree (First Cycle)
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Discussion, Drill and Practice.
Course Coordinator
Course Lecturer(s)
  • Asst. Prof. Dr. Hakan TORA
Course Assistants
Course Objectives Sensors, general information about sensor types and sensor working principles. What is a pattern? Pattern classification applications. Theory and methods of pattern classification. Feature extraction and selection. MATLAB Classification Learner Tool. Analysis and performance of classifiers. RFID basics.
Course Learning Outcomes The students who succeeded in this course;
  • Know about sensors.
  • Design a classifier system.
  • Analyze the performance of classifiers.
  • Design and implement a project including sensors.
  • Use the MATLAB Classification Learner application tool.
Course Content Sensors, general information about sensor types and sensor working principles; what is a pattern; pattern classification applications; theory and methods of pattern classification; feature extraction and selection; MATLAB Classification Learner Tool; analysis and performance of classifiers; RFID basics.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 What is a sensor? Obtain the reference books.
2 Sensor types. Review last week’s topics
3 Working principles of sensors. Review last week’s topics
4 What is a pattern? Review last week’s topics
5 Theory of pattern classification Review last week’s topics
6 Feature extraction. Review last week’s topics
7 Feature selection. Review last week’s topics
8 Analysis and performance of classifiers. Review last week’s topics
9 Midterm Exam Review all topics up-to this week
10 Design of an interdisciplinary project Review all topics
11 Project work continued Review all topics
12 Implementation of the project. Review all topics
13 Implementation of the project Review all topics
14 Presentations Review the project

Sources

Course Book 1. Duda, R. O., & Hart, P. E. (2006). 2nd Edition, Pattern classification. John Wiley & Sons.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 3 15
Presentation - -
Project 1 30
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 20
Final Exam/Final Jury 1 35
Toplam 6 100
Percentage of Semester Work
Percentage of Final Work 100
Total 100

Course Category

Core Courses X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 An ability to apply knowledge in mathematics and basic sciences and computational skills to solve manufacturing engineering problems
2 An ability to define and analyze issues related with manufacturing technologies
3 An ability to develop a solution based approach and a model for an engineering problem and design and manage an experiment
4 An ability to design a comprehensive manufacturing system based on creative utilization of fundamental engineering principles while fulfilling sustainability in environment and manufacturability and economic constraints
5 An ability to chose and use modern technologies and engineering tools for manufacturing engineering applications
6 An ability to utilize information technologies efficiently to acquire datum and analyze critically, articulate the outcome and make decision accordingly
7 An ability to attain self-confidence and necessary organizational work skills to participate in multi-diciplinary and interdiciplinary teams as well as act individually
8 An ability to attain efficient communication skills in Turkish and English both verbally and orally
9 An ability to reach knowledge and to attain life-long learning and self-improvement skills, to follow recent advances in science and technology
10 An awareness and responsibility about professional, legal, ethical and social issues in manufacturing engineering
11 An awareness about solution focused project and risk management, enterpreneurship, innovative and sustainable development
12 An understanding on the effects of engineering applications on health, social and legal aspects at universal and local level during decision making process

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 3 48
Presentation/Seminar Prepration
Project 1 20 20
Report
Homework Assignments 3 3 9
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 3 3
Prepration of Final Exams/Final Jury 1 3 3
Total Workload 131